AI agents are starting to move beyond answering questions and into the everyday work of large companies. They can search databases, call software tools, update customer records, review documents, investigate security alerts, write code, route support tickets, and complete multi-step workflows that once required several employees and several different applications.
That change creates a major new problem for enterprises. When AI software is allowed to make decisions and take actions, companies need to understand exactly what it did, why it did it, what information influenced the action, how much the process cost, whether the result was correct, and whether the system stayed inside the limits the business had set.
This is why AI observability is quickly becoming one of the most important infrastructure categories in enterprise technology.
Traditional software observability was built to answer fairly clear technical questions. Engineers wanted to know whether an application was running, why an API became slow, whether a database query failed, or where an error appeared inside a distributed system. Those questions are still important, but AI agents introduce a new class of problems because they do not always follow the same path every time they receive a request.
An AI agent may choose one tool for one customer and a different tool for another. It may search company data, call a model, change its plan, retry a failed operation, ask another agent for help, write information back into a business system, and decide for itself when the job is complete.
As a result, the company is no longer monitoring only whether software is available. It also has to monitor how software behaves.
That difference is creating a large new enterprise market around AI observability, AI evaluation, agent monitoring, governance, security, cost control, and workflow intelligence.
Boston has an important role in this shift.
Coralogix, which describes Boston as its headquarters, raised $200 million in June 2026 as it expanded its observability platform for a world increasingly shaped by AI agents. Dynatrace, whose principal executive offices are in Waltham, Massachusetts, has also expanded deeply into AI observability, agent monitoring, autonomous operations, and production evaluation.
Boston therefore has more than a passing connection to this market. The region already has deep experience in enterprise software, cloud infrastructure, cybersecurity, healthcare, financial services, robotics, biotechnology, and other industries where reliability and accountability matter greatly.
Those are exactly the environments in which companies cannot simply deploy autonomous AI and hope it behaves correctly.
They need visibility into what their agents are doing.
They need evidence that the systems are working.
They need ways to measure quality, cost, risk, and business outcomes.
In practical terms, they need a control layer for AI.
AI observability is starting to become that layer.
The Short Version: Enterprise AI Is Moving From Monitoring Software to Monitoring Decisions
Traditional monitoring mostly helps a company understand whether its software is working as expected. AI observability goes further because it must also help the company determine whether the software made the right decisions while completing the work.
A normal software request may follow a predictable path through an application server, database, API, and network layer. When something fails, engineers can often trace the problem to a slow query, an overloaded service, a broken API call, a deployment error, or a network issue.
AI agents behave differently because they have some freedom over how they complete a task. One request might require a single model call, while another could trigger retrieval, several tool calls, multiple models, an approval check, a sub-agent, and a write back into a business system.
OpenTelemetry’s work around generative AI observability shows how much more complex these systems are becoming. A single agent request can generate a top-level agent trace, multiple model spans, tool execution events, token data, latency measurements, and other telemetry that companies need in order to understand what happened.
The basic unit of reliability is therefore changing.
Traditional software teams often focused on the request.
Agentic AI teams increasingly need to understand the entire path the agent followed from the moment the request began until the business task ended.
That path is often called the agent trajectory.
LangSmith has increasingly focused on trajectory-level tracing because long-running agent sessions can include retries, tool calls, multiple user turns, planning steps, and handoffs between agents. Looking at a single model response is no longer enough because the important information is often hidden inside the sequence of decisions that produced the final result.
That is the direction the broader observability market is moving.
Enterprises do not simply need another place to store AI logs. They need something much closer to a flight recorder for autonomous software.
What AI Observability Actually Means
AI observability is the practice of collecting and analyzing enough information about an AI system to understand what it is doing in production, how it reached a result, and whether that result was acceptable.
For a modern agent, this usually means recording the sequence of important events that took place during a run. The record might include the model that was used, the instructions given to the model, the user’s request, the information retrieved from company systems, the tools the agent selected, the responses returned by those tools, the total latency, token consumption, costs, evaluation results, safety checks, approvals, handoffs, errors, and final business outcome.
Dynatrace describes AI observability in similar terms. Its platform focuses on collecting and correlating telemetry across AI models, agents, applications, infrastructure, and business systems so enterprises can understand agent execution paths, tool invocations, retrieval operations, latency, cost, reliability, and output quality.
The key idea is behavior.
Traditional infrastructure monitoring usually asks whether a system is healthy.
AI observability asks whether the system behaved correctly while doing its job.
Monitoring and observability solve different parts of the problem
Monitoring is still essential because companies need to track known risks and thresholds. A business may want an alert if an AI workflow becomes too slow, if costs suddenly rise, if an agent repeatedly fails to call a required tool, or if a security policy is violated.
Observability becomes especially valuable when the company does not already know what went wrong.
An engineer may open an agent trace and discover that the model understood the request correctly but retrieved outdated information. In another case, the retrieval step may have worked perfectly, but the agent selected the wrong software tool. A third failure may come from a tool that returned an error and caused the agent to retry the same operation repeatedly.
There is also a more difficult category in which every technical component works correctly while the overall task still fails.
That is one of the biggest differences between AI systems and traditional software.
A normal application may fail with a visible error.
An AI agent may fail while producing a polished, confident, and technically valid result.
Why Traditional Application Monitoring Is Not Enough
Consider a Boston financial-services company that builds an AI agent to prepare a client review. From the outside, the workflow may appear extremely simple because the customer asks a question and receives a detailed report.
Traditional application monitoring could show that the web request succeeded, the database remained healthy, the model API returned successfully, and the entire workflow took eight seconds. Every normal technical indicator might therefore suggest that the system worked.
The agent could still have produced the wrong result.
It might have used an outdated account balance, ignored one of the client’s accounts, searched the wrong date range, selected an inappropriate research tool, missed an internal policy requirement, or generated a claim that was not supported by the underlying data.
From the infrastructure team’s point of view, everything worked.
From the business’s point of view, the task failed.
That gap is where AI observability becomes essential.
AI creates semantic failures that normal monitoring cannot easily detect
Traditional software failures are usually mechanical. A service is unavailable, a connection times out, a request returns the wrong status code, or a piece of code throws an exception.
AI systems can fail semantically.
The system may return a perfectly valid response in the correct format, but the answer itself may be wrong. An agent may call an API successfully even though it should never have chosen that API. It may generate a persuasive explanation that does not answer the user’s actual question.
These failures are difficult because a green dashboard does not mean the AI performed well.
That is why evaluation is becoming closely connected to observability.
Traces explain what happened.
Evaluations help determine whether the result was good enough.
LangChain reported in 2026 that a large share of teams had already instrumented some form of agent observability, while fewer teams were consistently running online and offline evaluations. That difference matters because visibility alone does not tell a company whether the agent made a good decision.
Agents also create much longer failure chains
A simple chatbot may require one model call.
A production agent may require twenty or more steps.
A workflow could begin with a user request, pass through a planning model, query a customer database, search a document store, call an external tool, ask another model to review the result, send the output through an approval agent, update a CRM, and finally notify the customer.
If something goes wrong near the end, the real cause may have been introduced much earlier.
The wrong customer record could have been loaded at the start. A search query may have returned incomplete information. The agent may have misunderstood a tool result several steps before the final response.
Without an end-to-end trace, engineers are forced to investigate each system separately.
With a trace, the entire sequence can be reconstructed.
This is why concepts from distributed tracing are becoming so important for agentic AI. The difference is that spans now represent models, agents, memory operations, retrieval steps, evaluations, and tool calls instead of only servers and databases.
Multi-agent systems make visibility even more important
The complexity increases again when companies move from one agent to several.
A multi-agent system may have one agent responsible for planning, another responsible for research, another for compliance review, another for execution, and another for verification.
The final outcome depends on how all of those agents interact.
Coralogix has responded to this problem by introducing agent graph views that help teams understand which agent called another agent, which actions happened in parallel, where failures occurred, and how much different branches of the workflow cost.
This is an important product direction because a raw list of several hundred spans can quickly become impossible to understand.
As enterprise AI systems become more complex, observability products will increasingly have to show the workflow as a system rather than forcing teams to inspect individual events one by one.
BostonBizScene Original Research: How the AI Observability Market Is Converging
To understand how this market is developing, BostonBizScene reviewed publicly available documentation from a group of major AI observability and enterprise observability platforms in September 2026.
The goal was not to identify a winner or create a vendor ranking. Instead, the analysis focused on which capabilities are appearing repeatedly across different products and therefore may be becoming standard requirements for enterprise AI.
Our research methodology
We reviewed documentation and public product material from seven representative platforms:
| Platform | Main Position in the Market |
| Dynatrace | Enterprise full-stack observability |
| Coralogix | Full-stack observability and AI monitoring |
| Datadog | Cloud observability with agent monitoring |
| LangSmith | Agent tracing, development and evaluation |
| Arize Phoenix | AI observability and evaluation |
| Langfuse | Open-source LLM and agent observability |
| Comet Opik | Open-source AI observability and evaluation |
For every platform, we looked for clear evidence of several core capability groups. These included execution tracing, visibility into agent and tool activity, automated evaluations, production monitoring, standardized telemetry support, token or cost visibility, and some form of enterprise deployment or data-control option.
We counted a capability only when the platform’s own documentation clearly described it. This makes the research a snapshot of publicly documented product capabilities in September 2026 rather than a permanent feature comparison.
That distinction matters because this market is changing very quickly.
Features that are uncommon today may become standard within months.
Original Finding #1: Tracing Has Become the Basic Requirement
The clearest result from our analysis was that tracing is no longer an optional feature.
Every platform in the sample prominently supports some form of execution tracing or production workflow visibility.
This makes sense because almost every other observability function depends on having a detailed record of what the agent did.
Evaluations need runs to evaluate.
Cost systems need events to attribute spending.
Security teams need an activity history.
Developers need a step-by-step view when something fails.
Business leaders need a way to connect an AI workflow back to an outcome.
Share of reviewed platforms with core AI observability capabilities
| Capability | Presence Across Reviewed Platforms |
| Execution tracing | 100% |
| Agent or tool-step visibility | 100% |
| Quality evaluation or scoring | 100% |
| Production monitoring | 100% |
| Standard or open telemetry path | Strong majority |
| Token or cost visibility | Strong majority |
| Private or enterprise-controlled deployment | Mixed |
The more interesting finding is not simply that every platform has tracing.
The more important point is that tracing is becoming the foundation on which several other markets are being built.
An AI trace can now support debugging, evaluation, security investigation, cost analysis, compliance review, regression testing, and business-performance measurement.
That means control of the trace layer could become strategically important for enterprise software companies.
Original Finding #2: AI Evaluation and AI Observability Are Merging
Historically, model evaluation and production monitoring could be treated as separate activities.
That separation becomes less useful once AI systems are making decisions in production.
The trace tells the company what the agent did.
The evaluation tells the company whether the behavior was acceptable.
Those two functions naturally belong together.
Phoenix connects traces directly to datasets, experiments, and evaluations. Teams can examine what happened in production, identify a problem, score the run, move the example into a test dataset, make a change, and test whether the updated system performs better.
Langfuse follows a similar pattern by allowing production traces to be scored, converted into datasets, tested through experiments, and used in regression workflows.
LangSmith provides online evaluation tools that can score selected production runs continuously. Datadog supports evaluations at span, trace, and session level, allowing teams to decide whether they are judging one model call, the full workflow, or an entire multi-turn session.
Dynatrace also connects evaluation with production AI telemetry, while Coralogix increasingly combines monitoring, evaluation, security, and guardrails inside the same broader AI-management environment.
These products are arriving at a similar idea from different directions.
The emerging enterprise AI improvement loop
| Stage | Main Question |
| Trace | What happened? |
| Evaluate | Was the result acceptable? |
| Diagnose | Why did the failure occur? |
| Add to dataset | Can the problem be reproduced? |
| Improve | What part of the system should change? |
| Test | Did the change actually improve the result? |
| Deploy | Is the new version safe enough for production? |
| Monitor again | Does it continue to work under real conditions? |
This loop may become one of the most important operating models in enterprise AI.
Observability is no longer limited to watching production after software has already been released.
It increasingly feeds the entire development and improvement cycle.
Original Finding #3: OpenTelemetry Is Becoming Critical Infrastructure for AI Agents
One of the strongest signals in our research came from outside the vendor market itself.
OpenTelemetry is becoming increasingly important to AI observability.
The project was originally designed to give companies an open way to generate and move telemetry across different systems. Its role in AI is expanding because the industry badly needs common definitions for the enormous amount of data produced by agents and generative AI applications.
OpenTelemetry’s generative AI semantic conventions are designed to describe information such as model identity, agent operations, prompts, responses, tool execution, token usage, and other parts of the AI workflow in a more consistent way.
That may sound technical, but the business impact is significant.
Enterprises do not want every framework to produce incompatible telemetry
Imagine a Boston company using one model provider for customer support, another for internal research, LangGraph for orchestration, several MCP servers for tools, an internal agent framework, and a third-party AI product acquired through a merger.
If every part of that environment produces observability data in a different format, the company needs custom integrations everywhere.
That approach becomes expensive very quickly.
Common telemetry standards make it easier to move data across platforms, switch observability vendors, compare systems, and keep monitoring architecture stable even when models and frameworks change.
Dynatrace supports AI telemetry through OpenTelemetry’s OTLP interface.
Coralogix has added native support for OpenTelemetry generative AI semantic conventions.
Langfuse has moved important parts of its newer SDK architecture toward OpenTelemetry.
Opik supports direct OpenTelemetry ingestion, while Phoenix also exposes OpenTelemetry tracing endpoints.
The implementations still differ, and the standard itself continues to evolve, but the direction is becoming clear.
Enterprises increasingly want their telemetry layer to be more stable than the AI framework sitting above it.
Why Boston Has a Strong Position in This Market
Boston does not need every observability startup to be headquartered locally for the category to become important to the region.
The city and surrounding technology corridor already have many of the conditions that make AI observability valuable.
Boston has large healthcare systems, financial institutions, insurers, biotech companies, industrial technology firms, cybersecurity companies, universities, research institutions, robotics businesses, and enterprise software companies.
These organizations operate in environments where errors can be expensive and where leaders often need to explain what a system did.
That makes observability more important than it may be for a low-risk consumer AI application.
Dynatrace gives the region an established observability leader
Dynatrace lists Waltham, Massachusetts, as the location of its principal executive offices.
That places one of the world’s best-known observability companies directly inside Greater Boston’s enterprise technology ecosystem.
More importantly, Dynatrace is not treating generative AI as a minor extension to traditional monitoring.
Its AI Observability capabilities cover agent workflows, LLM usage, tool calls, retrieval, latency, cost, evaluations, infrastructure dependencies, and broader application behavior.
The company’s move into autonomous incident triage and remediation is also strategically important.
It suggests that observability platforms may increasingly move beyond simply showing people what happened.
They may start taking actions themselves.
That creates an interesting cycle.
Observability platforms begin using agents.
Those observability agents may eventually diagnose, recommend, or even fix problems involving other agents.
The market therefore becomes much larger than dashboards.
Coralogix is also making Boston central to its expansion
Coralogix raised $200 million in June 2026 and described the financing as support for its effort to build observability infrastructure for an environment in which both humans and AI agents operate complex software.
The company’s product development helps show how broad the category is becoming.
Its recent AI capabilities include OpenTelemetry-based ingestion, agent graphs, evaluations, guardrails, AI security-posture tools, code-agent monitoring, user-session correlation, and detailed cost and usage analysis.
The important point is that this no longer looks like a narrow LLM monitoring product.
It looks increasingly like an enterprise control system for AI activity.
Original Finding #4: The Most Valuable Layer May Be the One That Connects AI Behavior to the Rest of the Software Stack
AI-native observability companies have a clear advantage because they are designed around prompts, model calls, evaluations, agent workflows, datasets, and modern AI engineering.
Traditional observability companies have a different advantage.
They already monitor everything around the AI.
They can see databases, cloud infrastructure, APIs, network behavior, Kubernetes environments, applications, identity systems, user sessions, business transactions, and other services.
That broader view becomes extremely valuable because an apparent AI failure may not actually be caused by the model.
Imagine that an insurance agent suddenly takes twenty seconds longer to complete a policy workflow.
The problem could be the LLM.
However, it could also be a slow customer database, an overloaded vector store, an authentication issue, a failing MCP server, a third-party API, network congestion, or a prompt change that caused the agent to make four unnecessary tool calls.
A useful observability system needs to connect these layers.
Dynatrace has emphasized this cross-stack correlation by connecting AI telemetry with services, infrastructure, applications, logs, traces, and business events.
Coralogix is moving in the same direction by tying AI activity to wider application and user-session information.
This suggests that AI observability may not remain a separate software category forever.
It may eventually become part of the broader enterprise observability market.
The market appears to be developing around two different buyers
The first buyer is often the AI engineering team.
That team wants excellent traces, fast instrumentation, prompt visibility, evaluation tools, datasets, model comparisons, and straightforward debugging.
The second buyer is the enterprise platform organization.
That group cares more about access control, auditability, security, data retention, governance, cross-stack correlation, service reliability, cost attribution, and integration with existing operations.
Products that can satisfy both groups may have a major advantage as AI agents move from experiments into large enterprise environments.
AI Agent Economics Make Observability Even More Valuable
Observability has always helped companies understand the performance and efficiency of software.
AI agents add another dimension because every decision can have a direct model cost attached to it.
A single workflow may call several models, use multiple tools, retrieve documents, ask another agent for help, and retry operations after failures.
A poorly designed agent can therefore produce a correct result while still being economically inefficient.
Cost problems can remain invisible in a healthy system
Imagine a customer-service agent that costs an average of 14 cents per completed session.
A prompt change causes the agent to start calling a premium model three times instead of once.
The cost rises to 39 cents.
The agent still works.
Latency remains acceptable.
Customers notice no obvious change.
Traditional monitoring may therefore show nothing alarming.
At one million customer sessions, however, that small workflow change could create roughly $250,000 in additional AI expense.
That is why modern AI observability platforms increasingly expose token usage, model selection, tool behavior, and cost attribution.
The observability market is beginning to overlap directly with AI FinOps.
The Agent KPI Dashboard Boston Companies Should Build
Many companies begin measuring agents with the wrong metrics.
They track the number of questions answered, the number of employees using the tool, or the number of agent runs completed.
Those numbers are useful for understanding activity, but they do not prove that the system creates value.
A stronger dashboard should measure five different layers of performance.
Outcome metrics should come first
The most important question is whether the agent completed the business task.
A support agent should be measured on successful resolution.
A finance agent might be measured on reconciliations completed correctly.
An insurance agent could be measured on claims processed.
A security agent might be measured on investigations resolved.
The exact metric changes by workflow, but the principle stays the same.
An agent with perfect uptime is still a poor system if it does not complete useful work.
Quality metrics show whether the outcome was trustworthy
After measuring completion, the company must determine whether the result was correct.
Different workflows require different evaluation methods.
A customer-service agent might be scored for factual accuracy, resolution quality, policy compliance, and tone.
A finance agent might need checks for completeness, correct data use, numerical accuracy, and approval compliance.
Some evaluations can be deterministic.
Others may require human review or model-based scoring.
Execution metrics reveal how the agent reached the result
A company also needs to understand whether the workflow itself is efficient and stable.
Useful measures may include unnecessary tool calls, retry frequency, tool success rate, agent handoffs, workflow depth, repeated loops, retrieval failures, and the frequency with which humans need to intervene.
These metrics help identify poor process design even when the final answer is correct.
Performance metrics show the operational cost of autonomy
Latency remains important because slow agents create bad user experiences and can delay entire business processes.
Companies should measure total workflow time as well as model latency, retrieval latency, tool latency, tokens consumed, cost by model, and cost by completed task.
The important point is to avoid optimizing individual metrics in isolation.
A model that costs slightly more but dramatically increases successful completion may still produce a better economic result.
Risk metrics show whether the agent stayed inside its authority
The final layer should measure control.
Companies need visibility into blocked actions, sensitive-data exposure, policy violations, failed approvals, suspicious tool use, unusual execution paths, security alerts, and attempts to operate outside defined permissions.
As agents receive more authority, these metrics become increasingly important.
Cost Per Resolved Work Unit May Matter More Than Cost Per Token
Many AI teams focus heavily on token pricing because it is easy to measure.
That can become misleading.
The more useful metric is often the total cost of the workflow divided by the number of business tasks successfully completed.
We can call this cost per resolved work unit.
Consider two agents.
| Metric | Agent A | Agent B |
| Average model cost per attempt | $0.08 | $0.14 |
| Task completion rate | 52% | 88% |
| Cost per 100 attempts | $8 | $14 |
| Completed tasks | 52 | 88 |
| Approx. model cost per completed task | $0.154 | $0.159 |
Agent A initially appears much cheaper because its average model cost is only eight cents.
Once successful completion is included, however, the economics are almost identical.
If Agent B also reduces the number of cases that require employee intervention, it could easily become the less expensive business system overall.
This is why observability needs to connect technical telemetry with real outcomes.
Optimizing token use without measuring completed work can lead companies in the wrong direction.
Why Boston’s Regulated Industries Will Need Deeper Observability
Boston has a particularly strong concentration of industries in which AI actions cannot simply disappear into an opaque system.
Healthcare is one obvious example.
Financial services, insurance, biotechnology, cybersecurity, industrial technology, and public-sector work create similar requirements.
These organizations often need to explain not just whether software worked but how an important action occurred.
That turns observability into a form of operational evidence.
Agent traces may become audit records
An enterprise may eventually need to reconstruct exactly which model version ran, what instructions it received, which company information was retrieved, which tools were available, what tool the agent selected, what result the tool returned, which policy checks occurred, whether human approval was required, who provided that approval, and what information was changed in a downstream system.
That record has value far beyond engineering.
It can support security reviews, compliance investigations, risk management, incident analysis, and internal governance.
More visibility also creates a privacy problem
There is an important tradeoff.
Detailed traces make AI systems easier to understand and debug.
Those same traces can contain extremely sensitive information.
A trace may include the user’s prompt, retrieved documents, customer data, tool arguments, model responses, business records, or private internal instructions.
OpenTelemetry’s generative AI specifications recognize this issue because some telemetry can include complete prompts, completions, tool calls, and tool results when detailed content capture is enabled.
For regulated Boston organizations, “record everything” is therefore not a sensible observability strategy.
Companies need clear rules around what is captured, what is redacted, where traces are stored, how long the data remains available, who can access it, and which production environments should disable detailed payload collection.
Why Self-Hosting and Data Control Still Matter
The AI observability market also shows a meaningful difference in deployment philosophy.
Some tools are strongly cloud oriented, while others provide self-hosted or enterprise-controlled deployment options.
Langfuse can be self-hosted through Docker or Kubernetes and can run inside private environments.
Opik also supports self-hosted deployment.
Phoenix provides deployment options for private environments alongside OpenTelemetry-based tracing.
LangSmith has enterprise deployment paths for organizations that need greater infrastructure control.
These capabilities matter because AI traces may contain some of the most sensitive data generated by a modern enterprise application.
For an early-stage startup, a managed cloud service may be the fastest and simplest option.
For a hospital, bank, biotech company, defense contractor, or large enterprise, the question may be very different.
The telemetry system could contain exactly the information the company is most reluctant to send outside its own environment.
Deployment architecture therefore becomes a strategic buying issue rather than a minor technical detail.
AI Observability Is Becoming an AI Security Market
One of the most important second-order effects of agentic AI is the growing connection between observability and security.
Traditional software can cause damage when it contains vulnerabilities or receives malicious input.
Autonomous agents add another risk because they can take actions.
An enterprise agent might send emails, update customer records, issue refunds, run software, change permissions, access sensitive files, purchase services, or trigger production workflows.
Security teams therefore need visibility into the same execution history that developers use for debugging.
The first security question is usually simple: What happened?
Answering that question for an agent may require far more information than a traditional application log contains.
The security team may need the original instruction, retrieved context, model output, agent identity, tool identity, tool arguments, tool result, downstream API activity, policy checks, approval events, and the final change made to the business system.
That information looks remarkably similar to an AI observability trace.
Coralogix’s product direction makes this convergence particularly visible because its AI Center increasingly combines monitoring with evaluations, guardrails, agent analysis, and AI-security posture capabilities.
The division between AI observability, AI security, AI governance, and AI evaluation is therefore becoming less clear.
Over time, many enterprises may view them as parts of the same control architecture.
Human-in-the-Loop AI Also Needs Observability
Companies often respond to concerns about autonomous AI by saying that a person will review important actions.
That is useful, but simply adding a human approval step does not solve the monitoring problem.
The company still needs to understand where humans intervene and whether that review process actually works.
Observability should be able to show whether a person examined the evidence or simply clicked approve, how frequently employees override the agent, which types of decisions cause the most intervention, how long approval takes, and what happens when the responsible employee is unavailable.
If humans are repeatedly correcting the same category of mistake, the pattern should be visible.
That information can reveal where the agent needs improvement or where the workflow should remain permanently under human control.
Dynatrace’s 2026 research into agentic AI reported that a large share of agent decisions were still being human verified.
If that remains true across enterprise deployments, companies should monitor the human and the agent as one combined operating system rather than treating human review as something separate.
What Boston Companies Should Instrument First
A company does not need to collect every possible signal before it starts using AI observability.
In fact, trying to monitor everything at once can create enormous volumes of data without producing much useful insight.
A better approach is to start with one meaningful workflow and instrument the points that matter most.
Step 1: Give every important agent run a unique trace ID
Each request should have an identifier that follows the workflow across models, agents, services, tools, databases, and downstream systems.
That identifier creates the foundation for everything that follows.
Without it, engineers may be forced to piece together information manually from several different platforms.
Step 2: Capture major decision boundaries
Teams should instrument the moments at which the workflow meaningfully changes direction.
Those points usually include model calls, retrieval operations, tool selection, tool execution, agent handoffs, approval checks, writes to business systems, and the final outcome.
Capturing every tiny internal event may create unnecessary noise.
Capturing the major decisions gives teams enough information to reconstruct the workflow.
Step 3: Measure latency and cost at every important stage
Total workflow latency is useful, but it does not tell the team where the delay occurred.
The same problem applies to cost.
Companies should record model latency, tool latency, retrieval time, tokens used, and model cost at the stage where each event occurs.
This makes it much easier to identify inefficient parts of the system.
Step 4: Define success before building evaluations
Teams often create a large set of generic AI-quality scores before they have clearly defined what a successful business outcome looks like.
The order should be reversed.
First decide what the agent is supposed to accomplish.
Then create evaluations that measure whether it actually accomplished that task correctly.
Step 5: Turn real production failures into a test dataset
One of the most valuable habits an AI team can develop is saving meaningful production failures.
A real failure should not disappear after it is fixed.
The company should convert it into a regression test.
Future versions of the agent can then be tested against the same example.
This creates a feedback loop in which production makes the test system stronger over time.
Step 6: Connect the agent to business outcomes
Technical telemetry should eventually be linked to the results management cares about.
For a support agent, that might mean ticket resolution.
For a finance system, it could mean reconciliations completed correctly.
For an e-commerce agent, it could mean conversion or revenue.
For a security agent, it may mean incidents investigated or threats blocked.
This connection is what turns observability from a development tool into a business operating system.
A Practical 90-Day AI Observability Plan
Companies do not need a massive observability program before deploying their first useful agent.
They do, however, need a structured approach.
A focused 90-day plan can create a strong foundation.
Days 1-30: Make the workflow visible
Choose one meaningful production or near-production agent and map the major steps it takes.
Instrument model calls, important tools, retrieval, agent handoffs, latency, token usage, errors, and final outcomes.
Every run should receive a unique identifier.
By the end of the first month, a developer should be able to take an important agent run and reconstruct what happened without searching through several disconnected systems.
That is the first major milestone.
Days 31-60: Add quality measurement
Once the workflow is visible, review a meaningful sample of traces and identify the most common ways the agent fails.
Those failure patterns should become the basis of the evaluation system.
Some evaluations can be simple rules.
The team may check whether a required tool was called, whether an approval step was skipped, whether a response used the correct format, or whether a workflow reached the expected end state.
Other quality problems may require human review or model-based evaluation.
The company should also begin building a dataset from real production examples so changes can be tested before deployment.
Days 61-90: Add governance and business measurement
During the final month, the company should connect observability to operational control.
Teams can add alerts for abnormal cost, repeated loops, sudden latency increases, tool failures, declining evaluation scores, or attempts to perform unauthorized actions.
Data-retention rules should be defined.
Sensitive fields should be redacted where appropriate.
Access controls should be established.
Most importantly, the dashboard should begin reporting business-level measures such as successful tasks, human intervention rates, and cost per completed outcome.
At that point, observability becomes part of day-to-day operations rather than simply an engineering debugging tool.
How Boston Companies Should Evaluate AI Observability Platforms
It is easy to be impressed by a clean tracing interface.
That should not be the main buying criterion.
Most serious products can now display model calls and execution spans.
The more difficult questions determine whether the platform will remain useful after an agent moves into real enterprise production.
Can the platform reconstruct the entire workflow?
The observability system should ideally follow activity across models, retrieval, agents, APIs, tools, databases, services, and business applications.
If visibility ends at the LLM, many important production failures will remain outside the trace.
Can the platform evaluate results instead of only recording them?
A trace can show what happened.
The company also needs a way to decide whether the outcome was acceptable.
Look for support for online evaluations, offline evaluations, deterministic checks, human feedback, experiment tracking, datasets, and regression testing.
Does it support open telemetry standards?
AI models and frameworks are changing extremely quickly.
A company that chooses a new model provider six months from now should not need to rebuild its entire observability architecture.
Support for standards such as OpenTelemetry can reduce that risk.
Can it understand complex multi-agent workflows?
Ask what happens when an agent trace contains dozens of agents, hundreds of steps, parallel branches, retries, and sub-agent handoffs.
A system that works beautifully for a simple chatbot may become extremely difficult to use once enterprise workflows become more complex.
Can it protect sensitive information?
Enterprises should ask detailed questions about prompt capture, data redaction, encryption, retention, role-based access, audit logs, data residency, private networking, and self-hosted deployment.
These issues should be addressed before the company begins sending production traces into the platform.
Can it connect AI performance with normal application observability?
This capability may become increasingly important.
A useful platform should help a team determine whether a problem came from the model, the vector store, the application, the tool, the cloud infrastructure, the network, or an external API.
Without that context, teams may waste time blaming the AI system for problems that originated elsewhere.
Can it show the economics of the whole workflow?
Token usage is valuable, but it is not enough.
The company should ideally be able to understand cost by agent, workflow, business unit, customer, model, and successful task.
That is the level of visibility needed for serious enterprise cost management.
The Next Market Is Observability for Digital Workers
The language around AI agents may eventually change.
Enterprises may stop thinking of them primarily as models and start treating them more like software workers.
Once thousands of agents operate inside a business, management will need a new class of operational tools.
Leaders may want to know which agents complete the most work, which workflows create the best return, which agents require too much human intervention, which digital workers violate policy most often, which models are being overused, which tools create bottlenecks, and which agent changed a particular customer record.
These questions sound less like traditional software monitoring.
They sound like management questions.
Observability becomes the system that connects the two.
Original Finding #5: AI Observability Could Become the Management Plane for the Autonomous Enterprise
The most important conclusion from our analysis is that AI observability may eventually become much larger than a monitoring category.
The first generation of AI observability focused mainly on development.
Teams inspected prompts, model outputs, retrieval steps, and chains so they could find hallucinations and debug applications.
The next generation is much broader.
Platforms are beginning to monitor models, agents, tools, applications, infrastructure, security policies, costs, users, evaluations, approvals, and business results inside the same environment.
The generation after that could become an active management layer.
Instead of simply reporting what an agent has done, the observability system may help determine what the agent is allowed to do next.
A low-risk action could proceed automatically.
A medium-risk action could require additional evaluation.
A high-risk action could be sent to a person.
A suspicious action could be blocked.
An abnormal cost spike could pause a workflow.
A repeated failure could trigger an automatic fallback.
A badly performing model version could eventually be removed or rolled back without requiring engineers to notice the problem manually.
At that point, observability stops being passive.
It becomes part of execution.
Dynatrace’s movement toward autonomous incident triage and remediation gives us an early glimpse of this direction.
Coralogix’s expansion into evaluations, guardrails, agent graphs, security posture, and AI workflow monitoring points toward the same broader opportunity.
The software that watches AI agents may eventually become the software that governs them.
Why Boston Could Benefit as AI Trust Becomes More Important
Massachusetts is already investing heavily in applied artificial intelligence through efforts such as the Massachusetts AI Hub.
The region has powerful universities, large research institutions, venture capital, strong technical talent, enterprise software experience, and unusually deep industry knowledge in areas such as health, life sciences, cybersecurity, finance, and robotics.
The biggest Boston AI opportunity may therefore not be another general chatbot.
It may be building the infrastructure that allows serious organizations to deploy autonomous software safely.
Healthcare AI needs evidence.
Financial AI needs controls.
Biotech AI needs reproducibility.
Industrial AI needs reliability.
Security AI needs detailed activity records.
Enterprise agents need all of those things at the same time.
Observability sits underneath each requirement.
Five Developments That Could Shape the Next Phase of AI Observability
Tracing will increasingly become a default part of every serious agent framework
Observability is likely to move closer to the point where agents are built.
Developers will increasingly expect tracing to work automatically instead of being added months after a system reaches production.
The growth of OpenTelemetry support across the AI ecosystem already points in this direction.
Evaluations will become normal production telemetry
Companies currently treat evaluation as a specialized AI task.
That is likely to change.
In the future, an enterprise dashboard may show availability, latency, cost, task completion, policy compliance, and output quality beside one another.
Quality could become as normal a production signal as response time.
Open standards could reduce observability lock-in
Companies will continue changing models and frameworks as better options become available.
A standardized telemetry layer makes those changes easier.
That does not remove the value of commercial observability products.
Instead, competition may move toward better analysis, better evaluations, stronger workflow intelligence, better governance, and more useful automation.
Security and observability will increasingly merge around agent actions
Security teams care about who performed an action and why.
Agent observability records what autonomous software did.
The overlap is too strong to ignore.
Observability companies will continue adding security features, while AI-security companies will likely build deeper tracing and execution visibility.
Business completion will become more important than model benchmarks
Enterprise buyers are ultimately interested in completed work.
They want to know whether an agent resolved the ticket, processed the claim, completed the investigation, reconciled the account, or finished the workflow safely.
Model intelligence still matters.
However, enterprise value increasingly comes from what the whole system successfully completes.
The Bigger Story: The Enterprise AI Battle Is Moving Into the Control Layer
For the last several years, much of the AI industry focused on models.
Companies competed over intelligence, benchmark scores, reasoning ability, context windows, and model size.
Those questions still matter.
The enterprise market, however, is moving toward a more complicated challenge.
Most large organizations will have access to several capable models.
Their harder problem will be turning those models into dependable business systems.
That requires orchestration, memory, tools, permissions, evaluations, security, human approval, cost control, and observability.
The model provides intelligence.
The surrounding infrastructure determines whether that intelligence can safely perform real work.
This is why AI observability has the potential to become a very large enterprise market.
As software becomes more autonomous, visibility becomes more valuable.
As companies deploy more agents, it becomes harder to understand what is happening.
As agents receive greater authority, invisible mistakes become more expensive.
The underlying rule is simple:
Autonomy without observability will not scale.
Boston is well positioned to understand this shift because the region has spent decades building technology for industries in which software reliability matters.
Healthcare systems cannot tolerate unexplained failures.
Financial systems cannot operate without controls.
Cybersecurity products need detailed records.
Biotech systems require reproducibility.
Enterprise applications need accountability.
AI agents bring those old requirements into a new environment where software can make decisions and act with far more independence.
Someone still needs to understand what that software is doing.
Someone still needs to measure whether it is producing value.
Someone still needs to know when it goes wrong.
The companies that build that visibility, evaluation, and control layer may become some of the most important infrastructure businesses of the agentic AI era.
