DeepInspect vs AgentOps: Replaying What an Agent Did and Authorizing What It May Do

AgentOps was built for a debugging problem that conventional APM tools handle poorly. An agent that runs forty steps, calls six tools, and revises its own plan twice produces an execution shape that a request-response trace view flattens into noise. AgentOps renders it as a session you can replay with point-in-time precision, which is why teams running CrewAI, AutoGen, or the OpenAI Agents SDK adopt it early (AgentOps).

The platform tracks LLM and API spend per session, benchmarks agent performance, visualises multi-agent interactions, and surfaces failures. Enterprise deployments get SSO, self-hosting on AWS, GCP, or Azure, and the company cites SOC 2, HIPAA, and NIST AI RMF alignment.

Teams comparing it to DeepInspect are asking a governance question about agents. Both products care about agent traffic. One reconstructs what the agent did; the other decides what it may do.

TL;DR

AgentOps instruments agent frameworks to capture, replay, and analyse agent runs, with cost tracking and benchmarking built for multi-step autonomous systems. DeepInspect sits on the HTTP path between agents and model providers, evaluating identity-bound policy on every call and writing an audit record before the response returns.

AgentOps: the session as the unit

The design insight is treating the session rather than the request as the primary object. A session covers the whole agent run, and everything inside it nests: model calls, tool invocations, handoffs between agents, retries, and the final outcome.

Replay is where that pays off. When an agent burns 180,000 tokens and returns something wrong, stepping through the run to the decision point where it went sideways beats reading a log file. Cost tracking at session granularity answers the question finance actually asks, which is what a single customer interaction costs rather than what the monthly OpenAI bill totals.

Native framework integrations do the instrumentation for you. Add the SDK, and CrewAI or AutoGen internals report without manual span creation. That coverage of framework internals is the product's real technical achievement, and it is why a general-purpose observability tool feels thin next to it for agent work.

The product's position is in-process. AgentOps sees what the instrumented agent framework reports, and it holds no authority to stop a step.

Delegated authority at the call boundary

Agents create an authorization problem that predates observability. When a human asks an agent to reconcile invoices, the agent inherits some scope of authority, and the practical question is what bounds that scope on each individual model call it makes.

NIST's work on AI agent identity and authorization splits this into three pillars: agent identity, delegated authority, and action lineage. The application owns Pillar 1, establishing who the agent is. Pillars 2 and 3 need an enforcement layer at the AI call boundary, separate from the model and separate from the agent (NIST NCCoE).

DeepInspect implements those two pillars.

Every model call is evaluated on its own. An agent operating under a delegated scope that permits reading customer records cannot escalate to sending them to an unapproved provider partway through a run, because the scope is checked per call rather than granted once at session start. Static service credentials, which is how most agents authenticate today, grant permanent full access to the model API for any prompt and any data context, and that violates least privilege by construction. The mechanics are covered in agentic AI runtime security.

Action lineage is the record side: a structured entry per call showing which agent, acting for which principal, under which delegated authority, sent what class of data where, and with what outcome. Further detail is in AI agent action lineage.

Feature comparison

| Capability | AgentOps | DeepInspect | |---|---|---| | Session replay and time-travel debugging | Yes, the core strength | No | | Multi-agent interaction visualisation | Yes | No | | Framework-native instrumentation | Yes, CrewAI, AutoGen, LangChain | Not required | | Per-session cost tracking | Yes | Per decision | | Agent benchmarking | Yes | No | | Failure detection and alerting | Yes | Denials recorded | | Self-hosting | Yes | Yes | | SSO and enterprise access control | Yes, to the platform | Yes, plus per-request identity | | Blocking an agent's model call inline | No | Yes | | Per-call delegated authority evaluation | No | Core | | Prompt classification against custom data classes | No | Yes | | Coverage of agents built outside supported frameworks | No | Yes | | Audit record independent of the agent runtime | No | Core | | Primary buyer | AI engineering teams | Security and compliance |

The failure mode each one catches

Both products are watching the same agent, and the failures they surface differ.

AgentOps catches the agent that loops, the tool that returns malformed output, the handoff that drops context, and the run that costs 40 dollars when it should cost 40 cents. These are correctness and efficiency failures, and finding them without replay is painful.

DeepInspect catches the agent that reaches a model it was never authorised to call, the run that sends a data class outside its delegated scope, and the compromised or misconfigured agent identity making calls no principal asked for. These are authorization failures, and observability surfaces them only after the data has gone.

Speed decides which one prevents damage. Mandiant's M-Trends 2026, drawn from more than 500,000 hours of incident response, put the median handoff from initial access to a secondary threat group at 22 seconds, down from over eight hours in 2022 (Help Net Security). An agent executing at machine speed produces the same tempo problem. Replaying the session afterwards is forensics.

Pick AgentOps if

• You are actively building multi-step agents and debugging their behaviour daily.
• Session replay and step-level visibility are what you are missing.
• Per-session cost attribution matters to your unit economics.
• You run CrewAI, AutoGen, LangChain, or the OpenAI Agents SDK.
• Agent authorization is currently handled at the application layer to your satisfaction.

Pick DeepInspect if

• Agents need enforced, per-call limits on models and data classes.
• You need action lineage tying each call to a delegated authority for audit.
• Agents in your environment run on frameworks nobody standardised.
• NIST AI RMF, the EU AI Act, or a sector mandate applies to your agent deployments.
• A compromised agent identity must be stopped rather than reported.

Running both

The combination is natural because the layers are disjoint. AgentOps instruments the agent runtime for development and operations. DeepInspect sits on the model-call path for authorization and evidence.

Pointing the agent's model client at the DeepInspect endpoint keeps AgentOps replay intact while placing the policy decision upstream of each call. A denied call appears in the AgentOps session as a refusal with a policy reason, which gives the engineer the debugging signal and the security team the enforcement, without either owning the other's system.

DeepInspect

This is exactly what DeepInspect does. DeepInspect sits inline between your users or agents and the LLM APIs they call, evaluating identity, data classification, model authorization, and organizational policy for every request and response, then making a pass or block decision before traffic reaches the model.

For agent traffic specifically, that evaluation happens per call rather than per session, so delegated authority is checked at each step of a run instead of granted once at the start. Enforcement adds under 50 ms in internal testing against inference times of 500 ms to several seconds. Every decision writes a structured record containing the agent identity, the principal it acts for, the data classes detected, the policy version, and the outcome, committed before the response returns on storage the agent runtime cannot alter.

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