Agent observability companies, products & suppliers
Traces and evals for tool-using agents, not generic LLM logs. Jobs: tool-call traces; trajectory eval; agent session replay.
What is Agent observability?
Traces and evals for tool-using agents, not generic LLM logs. Jobs: tool-call traces; trajectory eval; agent session replay.
What problems does it solve?
Agent actions are invisible, so failures look like 'the model was wrong'.
Typical business use cases
- Tool-call traces
- Trajectory eval
- Agent session replay
Important capabilities
- Span of tools
- Session grouping
- Action diffs
What buyers should evaluate
- Whether actions are reconstructed
- PII in tool payloads
- Retention
Risks and governance considerations
Tool payloads that include customer records stored as traces.
Procurement checklist
- Payload redaction
- Retention
- Who can replay
Relevant AI Trustmark assurance
AI Trustmark independent findings appear only when an assessment or certificate exists. Category membership does not imply verification.
Products and providers
Open a product profile for its catalogue details, or use the provider link for the company profile. Claimed listings appear first, then by name. Payment never changes the order.
By AgentOps
AgentOps is an agent observability platform from Staf, Inc. for tracing, debugging and deploying AI agents. It covers spans, costs and failure debugging across agent runs, aimed at
By Arize AI
Arize AX Evaluation covers methods and tooling for evaluating LLM applications — checking whether answers are correct, grounded in evidence and complete tasks. Teams use evaluation
By Athina
Athina AI Inc. sells evaluation and monitoring for LLM applications, including traces and datasets. Public pages describe production quality checks, not image remixing. Athina is A
By HoneyHive
HoneyHive helps enterprises trace, evaluate, monitor, and improve production AI agents across teams, stacks, and sensitive data environments. HoneyHive is HoneyHive's platform, fro
By LangChain
LangChain enables every company to own their intelligence. Control, govern, and compound intelligence with an open agent engineering platform. Trusted by 7k+ organizations. LangSmi
By Langtrace
Langtrace is Langtrace's platform, from Scale3 Labs, Inc. Published at docs.langtrace.ai. Directory filing: Agent observability. Vendor legal name on this product record: Scale3 La
By Last9
Unified observability for the whole team — developers, SREs, and AI agents. Full visibility. No cost explosion. Single view for metrics, logs, traces, and APM. Last9 is Last9's pla
By Middleware
Middleware offers full-stack observability with real-time monitoring and diagnostics, helping teams detect issues at scale while keeping data secure. Middleware is Middleware's pla
By OpenObserve
High-performance, unified observability for the AI era. 140x lower storage cost. OpenObserve is OpenObserve's platform, from OpenObserve Inc. Published at openobserve.ai/blog/monit
By RagaAI
Empowering healthcare leaders with purpose-built AI Agent Suites for Radiology, Allergy, and Clinical Trials. Proven ROI and enterprise-grade reliability. RagaAI Catalyst is RagaAI
By Snowflake
Snowflake enables organizations to collaborate, build AI-powered data apps, and unlock data insights—all within a secure and scalable AI Data Cloud. TruLens is Snowflake's platform
Products
Claimed products appear first. Ranking packs and payment do not change this list.
- AgentOps· AgentOps
- Athina· Athina
- HoneyHive· HoneyHive
- LangSmith Observability· LangChain
- Langtrace· Langtrace
- Last9· Last9
- Middleware· Middleware
- OpenObserve· OpenObserve
- RagaAI Catalyst· RagaAI
- TruLens· Snowflake
Also used in this category
These products have a different primary category so they do not compete for the same ranking queries. They are listed here because buyers still encounter them in this job.
- Arize AX Evaluation· Arize AI
- LangSmith Evaluation· LangChain
Related categories
Relevant procurement and assurance guides
Frequently asked questions
What is Agent observability?
Traces and evals for tool-using agents, not generic LLM logs. Jobs: tool-call traces; trajectory eval; agent session replay.
What should not be listed as Agent observability?
Products whose buyer job is AI observability without tools, LLMOps suites, or security audit logs. Those belong on their own category page so search queries are not split.
Has AI Trustmark independently assessed every Agent observability supplier?
No. A category listing is descriptive. Independent assessment is shown only on company or product pages that carry Trustmark evidence.
What logging should an AI agent provide?
Buyers should be able to see who the agent acted as, which tool was called, what data was sent, what changed, and when. Logs that omit write actions or store raw customer prompts without access control are incomplete evidence.
What model or provider changes should a buyer insist on being told about?
Material change usually includes a new model family, new region, new subprocessor, new write-capable tool, or a change that affects logging, privacy or human oversight. Those changes should trigger evidence refresh rather than a silent release.
How should buyers verify where AI customer data is processed?
Ask for the named processing locations, cloud regions and any subprocessors that see prompts, files or outputs. A directory listing is not evidence of residency. Independent assessment records the locations that were in scope on the assessment date.
Does a TrustMark on one product cover the rest of the company?
No. Independent assessment is scoped to the named organisation and, where relevant, the named product. Category pages list suppliers as a topic label. They do not imply that every listed company has been assessed.
What incident-handling evidence is useful for AI suppliers?
Buyers should see how AI-specific failures are detected, contained and notified — including unsafe outputs, data leakage and unauthorised agent actions. An incident policy that never mentions models, prompts or tools is incomplete for this class of product.