--- Title: "The 12 best AI governance software tools for agents in 2026, from action controls to tracing" Url: "https://devrev.ai/blog/best-ai-agent-governance-observability-platforms" Published: "2026-10-22" Last Updated: "2026-10-22" Author: "DevRev Editorial" Excerpt: "Tracing tools explain what an agent did. Policy tools decide what it may do. We sorted 12 by which job each one does, and say which two to pair." Reading Time: 19 --- # The 12 best AI governance software tools for agents in 2026, from action controls to tracing **TL;DR** - AI governance software controls what an agent may do, and observability tools trace what it did. Most tools on this list are built for one of those jobs, so teams usually pair two. - Our pick for the control layer is Computer, by DevRev, for agents built on DevRev that change records. You choose which actions wait for a person’s sign-off, and every action is logged and can be undone. - For agents built elsewhere, pair a tracing tool (entries 7 to 12) with a policy tool (entries 4 and 5). - We grade all 12 on four records an auditor asks for: access, trace, policy, and change (what the agent changed, who approved it, and how to reverse it). Say an AI agent swaps the billing contact on your biggest customer’s account at 2:14 PM on a Thursday, so the next invoice goes to a stranger. A tracing tool can show which model it called and why, and a policy tool can show which rule was in force. Neither one, on its own, shows who approved the change before it ran or how to take it back. In a May 2026 forecast, Gartner [expects 40% of enterprises to demote or decommission autonomous AI agents by 2027](https://www.gartner.com/en/newsroom/press-releases/2026-05-26-gartner-says-applying-uniform-governance-across-ai-agents-will-lead-to-enterprise-ai-agent-failure) “due to governance gaps identified only after production incidents occur.” This is desk research: we read each vendor’s product pages, audit-log and tracing docs, pricing, and trust center on Friday, September 25, 2026. We checked every claim again on Friday, October 16, 2026, and deployed none of the 12 ourselves. Our own product is entry 1, held to the same tests as the rest. ## AI governance software and observability platforms compared The table grades four records: access, trace, policy, and change. Access is who signed in and who changed the tool’s own settings, and trace is what the model and agent did. Policy is what a guardrail allowed, blocked, or flagged, and change is what the agent changed, who approved it before it ran, and how to reverse it. A record counts when the vendor’s own pages describe it. Entry prices use different units (seat, user, trace, and credit), so they’re a starting line, not a cost ranking. Reddit and G2 quotes are one person’s view each, linked and dated; an entry without one had no conversation we could find. | Platform | Best for | Records it keeps | Traces or enforces, and where | Entry price | Compliance, per its trust page | | --- | --- | --- | --- | --- | --- | | Computer | Agents that change records, with approval before they act | Access, policy, and change, for agents built on DevRev; traces logged for your observability tools | Enforces: approval gates on the action, inside the agent platform | $30 Pro on our Computer site; “Contact us” on devrev.ai (Mini is free; Agent Studio on Pro) | SOC 2 Type 2, ISO/IEC 27001:2022, HIPAA | | Salesforce Agentforce | Agents that live only in Salesforce | Access, trace, policy; part of change (step variables, no approver) | Both, inside the agent platform | Foundations $0; Flex Credits $500 per 100,000 credits | ISO 42001 (Agentforce) | | Superblocks | Checking what business teams build before it ships | Access (Enterprise); policy at publish time | Enforces at publish time, inside its build platform | Teams $100 a month, billed yearly ($125 billed monthly) | SOC 2 Type II, HIPAA | | Credo AI | A policy and evidence program across every AI system | Policy; access via export; traces ingested | Sets policy, beside the agent | Not published | SOC 2 Type II, TX-RAMP | | IBM “watsonx.governance” | AI risk inside enterprise GRC workflows | Policy; access via Activity Tracker; trace for Orchestrate agents | Sets policy beside the agent, plus guardrails | Risk & Compliance Basic from $3,500 a month (IBM Cloud) | Not SOC 2 certified, per IBM’s security overview | | Microsoft Purview | Copilot and Copilot Studio in a Microsoft estate | Access, trace, policy; part of change (read, create, modify) | Both, inside Microsoft 365, plus endpoint DLP | Purview Suite $12 per user a month, paid yearly, with E3 | ISO 27001 (Purview portal) | | Fiddler AI | Inline guardrails for model-risk teams | Access, trace, policy; approval for high-risk decisions | Both: guardrails in front of the call, traces beside it | Guardrails free; Developer $0.002 per trace | SOC 2 Type II, HIPAA | | LangSmith | Finding out why an agent chose a step | Access (Enterprise), trace, policy (Gateway); approval before action (Fleet) | Traces beside the agent; Gateway (beta) enforces in front | Developer $0; Plus $39 per seat a month (cloud) | SOC 2 Type II, ISO 27001, HIPAA | | Langfuse | Running the trace store yourself | Access (Enterprise), trace, policy (scores) | Traces and scores, beside the agent | Hobby free; Core $29 a month (cloud) | SOC 2 Type II, ISO 27001, “HIPAA-ready” region | | Arize | Teams with an ML monitoring practice | Access (Enterprise), trace, policy (guard verdicts) | Traces beside the agent; guards run in your code | AX Free; AX Pro $50 a month (SaaS) | SOC 2 Type II, ISO 27001, HIPAA, PCI DSS | | Braintrust | Making evals routine before every release | Access (Enterprise), trace, policy (scores) | Traces and scores, beside the agent | Starter $0; Pro $249 a month | SOC 2 Type II, HIPAA | | W\&B Weave | Teams that train models on W\&B | Access (Enterprise), trace, policy (guardrail scores) | Traces beside the agent; guardrails run in your code | Free; Pro from $60 a month, billed monthly | SOC 2 Type II, ISO 27001, HIPAA | ### 1. Computer, by DevRev – approval, audit, and undo where the agent acts ![Screenshot for 1. Computer, by DevRev – approval, audit, and undo where the agent acts](https://cdn.sanity.io/images/umrbtih2/production/cde7effec687bcf34d8e6201623ebb8c26de5ccd-624x352.png) Computer is our AI teammate, and for agents built on DevRev it’s the governance and action layer: the agent, the approval gate, and the change record live in the same place. Our [“How Computer works” page](https://devrev.ai/how-computer-works) says it in one breath: “Set approval gates on any action. Sensitive operations – refunds, account changes, external communications – require human sign-off before execution, not after.” It also promises “a full audit trail for every action, every trace, and every trigger,” and tells you to send traces and guardrail logs “to the observability tools you already use.” Akhil Kintali, from our product marketing team, names the gap in our AI governance guide: “Most of what gets sold as AI governance today watches agents after they act. That is monitoring. It is not control.” – [Akhil Kintali, DevRev](https://devrev.ai/blog/ai-governance) You configure the gate yourself. In [Agent Studio’s reference](https://support.devrev.ai/en-US/devrev/article/kIjLyjeT-agent-studio-reference), each skill carries a “Needs Approval” setting and, by default, runs with the permissions of whoever the agent is acting for. Three parts of Computer are worth keeping apart. Connectors bring data in: our [Okta connector](https://support.devrev.ai/en-US/devrev/article/mKHCupBE-okta-airsync-connector) imports users, groups, and audit logs, and says “Data flows from Okta to DevRev only (no write-back).” Actions run through Agent Studio skills, which are built-in tools, natural-language skills, or workflows, and agents can also reach outside systems over the Model Context Protocol (MCP). Our [help center](https://support.devrev.ai/en-US/devrev/article/LhjCUxm2-agent-studio) lists Slack, WhatsApp, Plug Chat, and email for customer experience agents. The record behind each action has three layers: API audit logs, the Safe Actions record, and undo. Our [audit logs](https://support.devrev.ai/en-US/devrev/article/_PyPN5SD-audit-logs) capture “all API calls performed in DevRev, recording who accessed what and when,” and our [trust center](https://security.devrev.ai/) lists SOC 2 Type 2, ISO/IEC 27001:2022, and HIPAA. Safe Actions, as [our observability guide](https://devrev.ai/blog/ai-agent-observability) describes it, logs every agent action with the permission context at execution time and the outcome. It also keeps an immutable record of who or what authorized it. Neelabja Adkuloo, a member of our marketing staff, defines the change record in our guide to AI agent guardrails: “The audit record should distinguish an attempted action, the enforcement decision, any approval, and the observed execution outcome.” – [Neelabja Adkuloo, DevRev](https://devrev.ai/blog/ai-agent-guardrails) For the way back, [our homepage](https://devrev.ai/) says “Every action is logged, and can be undone,” and [our safe actions post](https://devrev.ai/blog/safe-actions) adds that “Individual actions can be undone cleanly, without cascading effects.” Michael Machado, CVP Product and Brand at DevRev, drew the line between a demo and production in our Agent Studio post: “A demo proves an agent can answer one question well. Production asks whether it answers ten thousand questions well, escalates the ones it should not handle, and leaves a record you can audit when something goes wrong.” – [Michael Machado, CVP Product and Brand at DevRev](https://devrev.ai/blog/agent-studio) Our closest customer story comes from BILL, a U.S.-based financial technology platform for small and midsize businesses. It’s in customer support rather than IT. [BILL’s story](https://devrev.ai/customers/bill) says it operates in “one of the world’s most regulated markets” and ran a “proof of concept using 200,000 real customer queries” before rollout. Its agent could “authenticate users, create and update Salesforce cases,” and the story lists “built-in guardrails” and “real-time monitoring and reliability controls” among its top features. [This DevRev video](https://www.youtube.com/watch?v=c-dVranwmD4), from our own channel, shows our product team taking an agent from prompt to production. **Best for –** support and operations teams whose agents change records, such as refunds, cases, or account settings. They want a person to approve the risky actions first. **What it records –** access, policy, and change, for agents built on DevRev. That means API audit logs, guardrail logs, and an action record of who or what authorized each action, with undo, plus session traces you can send to your observability tools. **Pricing –** Agent Studio starts at Pro. Pro is listed at $30 a month on [our Computer site](https://computer.io/pricing), with a discount for annual billing. Pro and Max both read “Contact us” on our devrev.ai [pricing page](https://devrev.ai/pricing), which also lists a free Mini plan. We bill in consumption-based credits. **Limitations** - Computer governs agents built on DevRev. Agents running on LangChain, Bedrock, or Vertex AI belong with the tracing tools in entries 7 to 12. - Approval is per skill and off by default, so nothing waits for a person until you switch it on. - We built Agent Studio for customer experience, support, and operations, as our launch post says. As of October 2026, our help center lists internal agents for employees as “coming soon.” Pick one refund or account change your agent should never make alone. We’ll show Computer holding it for sign-off with “Needs Approval” switched on, then open the Safe Actions record of who authorized it. [Book a demo →](https://devrev.ai/demo) ### 2. Salesforce Agentforce – traces and review gates inside Salesforce ![Screenshot for 2. Salesforce Agentforce – traces and review gates inside Salesforce](https://cdn.sanity.io/images/umrbtih2/production/5d0bc8233519975661537d4f712ba9886356955b-624x499.png) If every agent you run lives inside Salesforce, [Agentforce](https://www.salesforce.com/agentforce/) traces it from inside the platform. Salesforce says its session tracing data model, which covers each large language model (LLM) call, logs “every interaction, including user inputs, agent responses, reasoning steps, LLM calls, and guardrail checks.” Each action step keeps the state of its variables before and after it ran. Its trust principles add “Pause-for-review gates” before high-impact actions. **Best for –** teams whose agents run only on Agentforce and whose records already live in Salesforce. **What it records –** access through Setup Audit Trail, and trace and policy through session tracing and Trust Layer scores. It also keeps part of the change: step variables before and after, with no approver named. **Pricing –** Salesforce’s Agentforce pricing lists Foundations at $0, Flex Credits at $500 per 100,000 credits, and conversations at $2 each. In July 2026, Salesforce said Agentforce Observability is “now unmetered for every customer across all Agentforce SKUs,” though building new dashboards or bringing in outside data is still metered. **Limitations** - It observes Agentforce agents; agents built elsewhere need Salesforce’s MuleSoft Agent Fabric. - The Session Trace OpenTelemetry (OTel) API is in beta: one session per request, from the previous 72 hours. - The trace data needs Enterprise, Performance, or Unlimited Edition plus an Einstein add-on. One developer on _r/salesforce_ tested tool allowlisting and liked what held: “I exposed 4, allowlisted 3, withheld the write tool, and the agent genuinely can’t touch the one I held back. Platform-enforced, no Apex. That part I’d ship.” _u/EvolvinAI29._ [r/salesforce, June 2026 ↗](https://www.reddit.com/r/salesforce/comments/1twm4pj/) ### 3. Superblocks – governance applied before the app ships ![Screenshot for 3. Superblocks – governance applied before the app ships](https://cdn.sanity.io/images/umrbtih2/production/01d21ddc000c9e5d3372f699eee116b4370505a2-624x198.png) [Superblocks](https://www.superblocks.com/) describes its job as governance “applied at build time, before agents ever go live.” Business teams build apps and agents, and IT sets policies that can block a publish. Its docs add: “Every run, finding, remediation attempt, escalation, and ‘Publish anyway’ is recorded as an audit log event.” **Best for –** IT teams that let business teams build AI apps and agents, and want policy checked before anything reaches production. **What it records –** access, and policy at publish time. We didn’t find an agent’s “prompt-and-tool-call” trace on its public pages, so ask Superblocks if you need one. **Pricing –** Superblocks lists Teams at $100 a month billed yearly, or $125 billed monthly. Enterprise is custom. **Limitations** - Audit logs are an Enterprise feature. - It governs what’s built on Superblocks, and its own governance roundup sends you elsewhere for agents built outside it. - Its docs say “New policies default to Advisory,” so critical findings don’t stop a publish until you change the mode. ### 4. Credo AI – the policy and evidence program, built first ![Screenshot for 4. Credo AI – the policy and evidence program, built first](https://cdn.sanity.io/images/umrbtih2/production/f2fb1f57fba457605162da874cf6d90848364a41-624x350.png) [Credo AI](https://www.credo.ai/) is the tool for the team writing the rulebook before the agents arrive. It turns regulations into per-system controls, with “pre-built policy packs for EU AI Act, NIST AI RMF, ISO 42001, and SOC 2 with automated governance workflows and audit-ready evidence,” plus an agent registry. Credo says Forrester named it a Leader in [its Q3 2025 AI Governance Solutions Wave](https://www.forrester.com/report/the-forrester-wave-tm-ai-governance-solutions-q3-2025/RES184849). Gartner’s [June 2026 Magic Quadrant for AI Governance Platforms](https://www.gartner.com/doc/reprints?id=1-2NLSNXQV&ct=260625&st=sb) places it as a Visionary. **Best for –** governance, risk, and compliance (GRC) teams building one inventory and evidence program across every AI vendor, model, and agent. **What it records –** policy, with approvals and evidence per AI system, and access through audit log export. It takes in traces from your observability tools instead of producing them. **Pricing –** not published. Its site routes buyers to “Talk to an Expert” or “Request demo.” **Limitations** - Credo labels enforcement integration with continuous integration and delivery (CI/CD) pipelines, cloud access security brokers (CASBs), and API gateways as “planned.” - Trace-level evaluation depends on the observability tools you already run. - Its audit log documentation sits behind a request-access form on its trust center. ### 5. IBM “watsonx.governance” – GRC-grade workflows with a published price ![Screenshot for 5. IBM “watsonx.governance” – GRC-grade workflows with a published price](https://cdn.sanity.io/images/umrbtih2/production/b389ec30acefea8a69cb838bfd5c2e996bff09d6-624x321.png) [IBM “watsonx.governance”](https://www.ibm.com/products/watsonx-governance) puts AI risk inside the same governance console as every other enterprise risk, and it publishes a price ladder. Its product page promises to “track governance activities, decisions, and model changes with end-to-end traceability,” and [Gartner’s June 2026 Magic Quadrant](https://www.gartner.com/doc/reprints?id=1-2NLSNXQV&ct=260625&st=sb) names IBM a Leader. **Best for –** large enterprises that want AI risk assessments, factsheets, and approvals inside existing GRC workflows (IBM OpenPages). **What it records –** policy through Risk & Compliance, access through IBM Cloud Activity Tracker, and trace for agents hosted on watsonx Orchestrate. **Pricing –** on IBM Cloud, IBM lists Model Management from $0.64 a resource unit on pay-as-you-go pricing, where one model evaluation uses one unit, and Risk & Compliance Basic from $3,500 a month. A self-managed software edition is priced by virtual processor cores. **Limitations** - Agent monitoring requires watsonx Orchestrate, and IBM’s docs note that “Monitoring requires manual activation.” - IBM’s own security overview states: “Watsonx.governance is not SOC2 certified.” - Agent dashboards and alert routing aren’t customizable in the current release. ### 6. Microsoft Purview – Copilot audit inside the unified audit log ![Screenshot for 6. Microsoft Purview – Copilot audit inside the unified audit log](https://cdn.sanity.io/images/umrbtih2/production/c6e4829afbf8734cf7b7f4f5736cf7ee722f1436-624x470.png) If your agents are Microsoft 365 Copilot and Copilot Studio, and auditing is on in your tenant, [Purview](https://www.microsoft.com/en-us/security/business/microsoft-purview) is already recording them. Microsoft’s docs say: “Like other activities, prompts and responses are captured in the unified audit log.” Each record carries the agent ID, the files it touched, and any policy block. For Microsoft 365 resources, an Action field marks “read, create, modify.” **Best for –** Microsoft-standardized companies governing Copilot and Copilot Studio agents inside Microsoft 365. **What it records –** access, trace, and policy in the unified audit log, plus part of the change for Microsoft 365 resources, with no approver recorded. **Pricing –** Microsoft lists Microsoft 365 E5 at $60 per user a month and the Purview Suite at $12 per user a month on top of E3, both paid yearly. Audit of non-Microsoft AI apps is pay-as-you-go at $0.015 per 1,000 records. **Limitations** - Audit for third-party AI apps stays off until you switch it on. - Microsoft lists ChatGPT Enterprise and Claude Enterprise agents as unsupported for compliance management. - Model names are missing from Microsoft 365 Copilot records. One admin on _r/MicrosoftPurview_ finds the data in Purview’s Data Security Posture Management (DSPM) view. Getting it into a security information and event management (SIEM) tool is the catch: “I can see the information I want in DSPM, but having to manually review that data instead of pulling it into our SIEM is operationally inefficient.” _u/Stunning-Flower-7802._ [r/MicrosoftPurview, January 2026 ↗](https://www.reddit.com/r/MicrosoftPurview/comments/1qb5dlf/comment/nz97q9y/) ### 7. Fiddler AI – inline guardrails with a model-risk pedigree ![Screenshot for 7. Fiddler AI – inline guardrails with a model-risk pedigree](https://cdn.sanity.io/images/umrbtih2/production/cd467c67db5039abf53a5b7cb9e3df983b4cd84a-624x503.png) [Fiddler](https://www.fiddler.ai/) is a tracing vendor that also stands in the request path. Its guardrails run on its own task-specific models, which can sit inside your environment on the Enterprise plan, at “\<80ms In-environment enforcement latency.” Fiddler adds: “Every enforcement decision is recorded with who triggered it and what happened.” Its explainability roots in model risk management make it a natural fit for banks working under the [Federal Reserve’s model-risk guidance, SR 11-7](https://www.federalreserve.gov/boarddocs/srletters/2011/sr1107.htm). **Best for –** regulated model-risk teams that need guardrails and explainability beside the trace. **What it records –** access, trace, and policy, with enforcement decisions attributed. It also describes human approval for high-risk decisions. **Pricing –** Fiddler lists free guardrails, Developer at $0.002 per trace, and Enterprise on request. **Limitations** - Virtual private cloud (VPC) and on-premises deployment are Enterprise only. - The trace Explorer is in public preview, without bulk export. - API keys have “no per-key scope reduction,” so no read-only keys. ### 8. LangSmith – the trace that explains why the agent chose ![Screenshot for 8. LangSmith – the trace that explains why the agent chose](https://cdn.sanity.io/images/umrbtih2/production/bb3d0fa259cbe680add2ed31871512b9f5f6e199-624x315.png) When the question is why an agent picked that tool at step ten, [LangSmith](https://www.langchain.com/langsmith)’s trace is built to answer it. It works beyond LangChain, too: it traces other software development kits (SDKs) and speaks OpenTelemetry. Its audit logs are “a tamper-resistant record of administrative and configuration actions,” its LLM Gateway records which policies each call passed or broke, and Fleet agents can “pause and request approval before taking specific actions.” **Best for –** engineering teams debugging agents across frameworks, above all LangChain and LangGraph shops. **What it records –** access on Enterprise (400 days of logs), trace in depth, and policy through the Gateway. Fleet adds approval before action. **Pricing –** on its cloud plans, LangSmith lists Developer at $0 per seat with 5,000 base traces and Plus at $39 per seat a month. Self-hosted and hybrid are Enterprise only. **Limitations** - Audit logs are Enterprise only. - LangSmith keeps base traces for 14 days unless you pay for extended retention. - LangSmith’s docs say “The LLM Gateway is in beta.” A lead consultant at a small firm, reviewing LangSmith on G2, names the problem its trace solves: “With multi-step workflows, looking only at the final response makes it difficult to identify whether the problem came from the model, a prompt, a tool call, or another step in the workflow.” _Md A., Lead Consultant in a small business._ [G2, August 2026 ↗](https://www.g2.com/products/langsmith/reviews/langsmith-review-13288383) ### 9. Langfuse – open-source tracing you can run unplugged ![Screenshot for 9. Langfuse – open-source tracing you can run unplugged](https://cdn.sanity.io/images/umrbtih2/production/733d667d4e5f79e244ccc3fd31a0fb8c33a9a534-624x387.png) [Langfuse](https://langfuse.com/) says you can run it on your own hardware with the network cable pulled out: “Langfuse Self-Hosted can be run fully offline/air-gapped.” ClickHouse, the database company, [acquired it in January 2026](https://siliconangle.com/2026/01/16/database-maker-clickhouse-raises-400m-acquires-ai-observability-startup-langfuse/), and Langfuse’s founders wrote that “Langfuse stays open source and self-hostable.” The core is MIT-licensed. Its audit logs, described as “immutable records of all significant activities,” need the Enterprise tier. **Best for –** teams that must keep traces on their own infrastructure, including air-gapped environments. **What it records –** access on Enterprise, trace in depth, and policy as evaluator scores after the fact. **Pricing –** Langfuse Cloud lists Hobby free, Core at $29 a month, Pro at $199 a month, and Enterprise at $2,499 a month. Self-hosted open source is free. **Limitations** - Audit logs aren’t on Hobby, Core, or Pro. - Hobby keeps 30 days of data access and Core keeps 90. - Government hardening is listed as “in development.” Langfuse comes up as an open-source pick on _r/LangChain_. In one March 2026 thread, a developer named what running it yourself takes: “Langfuse \= nightmare to self host (Postgres, ClickHouse, Redis, and MinIO).” _u/Budget_Bar2294._ [r/LangChain, March 2026 ↗](https://www.reddit.com/r/LangChain/comments/1rjktte/comment/o8ofgkl/) ### 10. Arize – evals and tracing from the OpenInference authors ![Screenshot for 10. Arize – evals and tracing from the OpenInference authors](https://cdn.sanity.io/images/umrbtih2/production/727de4e003cd7409a57527c426b33d5d466b0bda-624x353.png) [Arize](https://arize.com/)’s team founded OpenInference, an OpenTelemetry-based open standard for AI tracing. It sells two ways in: Phoenix, source-available under the Elastic License 2.0, and the managed Arize AX. Dynatrace, the observability company, [agreed to buy Arize for $915 million](https://www.forbes.com/sites/janakirammsv/2026/08/13/dynatrace-pays-915-million-to-move-ai-evaluation-upstream/) in August 2026 and [completed the deal on October 1, 2026](https://www.developer-tech.com/news/dynatrace-completes-arize-acquisition-for-ai-observability/). **Best for –** teams that already run a machine learning (ML) monitoring practice and want agent evals in the same place. **What it records –** access on Enterprise (logins, changes, and exports), trace in depth, and policy as guard verdicts recorded in the trace. **Pricing –** Arize lists AX Free and AX Pro at $50 a month, both SaaS. Phoenix is free to self-host. **Limitations** - Retention is 15 days on AX Free and 30 on AX Pro. - Self-hosting AX is Enterprise only. - Its audit log docs warn that the “operationName” field “is client-supplied.” ### 11. Braintrust – the same scorers before and after release ![Screenshot for 11. Braintrust – the same scorers before and after release](https://cdn.sanity.io/images/umrbtih2/production/1ed57869bead01ae0d203715c569bfee9b2496ce-624x450.png) [Braintrust](https://www.braintrust.dev/) makes evaluation boring, which is a compliment. Its docs say “The same scorers you use in offline experiments run automatically on production traffic.” Its audit log keeps before-and-after diffs of admin changes. **Best for –** product teams that want every release gated by evals and every production failure turned into a test. **What it records –** access on Enterprise, trace in depth, and policy as scorer results. **Pricing –** Braintrust lists Starter at $0 and Pro at $249 a month. **Limitations** - Audit logs are Enterprise only. - Retention is 14 days on Starter and 30 on Pro. - The hosted Gateway is in public preview with no service-level agreement (SLA). A reviewer at a small software company describes on G2 what Braintrust does for their team: “Braintrust helps us evaluate and track improvements on our LLM prompts and pipelines. This platform makes it easy to collaborate and experiment on new changes quickly.” _Verified user in computer software at a small business._ [G2, February 2025 ↗](https://www.g2.com/products/braintrust-2024-12-22/reviews/braintrust-review-10789859) ### 12. W\&B Weave – agent traces beside the models you train ![Screenshot for 12. W\&B Weave – agent traces beside the models you train](https://cdn.sanity.io/images/umrbtih2/production/b76d298f53c853663aed24c6a160c812fcd8d6c6-624x297.png) [Weave](https://wandb.ai/site/weave/) suits agents built by the people who also train models. The traces live beside the experiments and artifacts in Weights & Biases (W\&B). CoreWeave, the AI cloud provider, [agreed to buy W\\&B in March 2025](https://www.reuters.com/markets/deals/nvidia-backed-cloud-firm-coreweave-acquire-ai-developer-platform-weights-biases-2025-03-05/) and now sells W\&B under its CoreWeave Forge brand. **Best for –** ML teams already on W\&B that want agent sessions, tools, and evals in the same system. **What it records –** access on Enterprise (API only), trace, and policy as stored guardrail scores. **Pricing –** CoreWeave lists Free and Pro from $60 a month, billed monthly. **Limitations** - Audit logs are Enterprise only on multi-tenant cloud. - Pro is only for companies with fewer than 50 employees. - Guardrails “require code changes” and aren’t supported in the TypeScript SDK. In a May 2026 _r/MachineLearning_ thread about W\&B’s new master service agreement, one developer weighed sending production traces to Weave: “Weave gives me a place to store prompts and a playground to run experiments. I was going to put some of my prod agent traffic into their traces, but I have some reservations now.” _u/algorithm477._ [r/MachineLearning, May 2026 ↗](https://www.reddit.com/r/MachineLearning/comments/1t5qq1a/comment/okcm77d/) Go back to that billing contact swapped at 2:14 PM. Whatever you shortlist, ask each vendor to show you who approved that change before it ran and how you’d take it back, because that’s the record an auditor asks for. Bring the change you’d most want to take back after an agent has made it, and we’ll show you Computer undoing that one action, without cascading effects. [Book a demo →](https://devrev.ai/demo) ## FAQ ### What is AI governance software? AI governance software decides what an AI system may do and proves what it did. It covers the inventory of AI systems, the policies and approvals around them, the logs of their behavior, and the controls that stop or reverse an action. Observability tools cover the logs, and governance tools cover the policy. Few cover the action itself. ### Does an LLM trace count as an audit trail? In part. A trace shows what the model and agent did, step by step, which explains why it happened. An auditor also wants the change the agent made in a system of record, the approval dated before it, and a way to reverse it. Ask each vendor which of those three its records hold. ### Can you roll back what an AI agent did? Some tools publish a way to. Computer publishes per-action undo for agents built on DevRev, and Rubrik Agent Cloud, from the data security company Rubrik and not graded here, publishes undo for agent actions across frameworks. Restoring an earlier agent or prompt version is a different thing: it prevents the next mistake but doesn’t reverse the last one. ### How much does AI governance software cost? Entry prices don’t compare line for line. Langfuse Core is $29 a month, LangSmith Plus is $39 per seat a month, and Braintrust Pro is $249 a month. IBM’s Risk & Compliance Basic starts at $3,500 a month. Gartner forecasts that spending on AI governance platforms will reach $492 million in 2026 and pass $1 billion by 2030. Gartner forecasts: https://www.gartner.com/en/newsroom/press-releases/2026-02-17-gartner-global-ai-regulations-fuel-billion-dollar-market-for-ai-governance-platforms. ### When do the EU AI Act’s logging rules apply to AI agents? Only agents that count as high-risk AI systems fall under the Act’s logging and human-oversight articles. After the Digital Omnibus on AI, adopted in July 2026, those obligations apply from December 2, 2027, for Annex III systems. For high-risk systems covered by the EU product-safety laws in Annex I, they apply from August 2, 2028. Digital Omnibus on AI: https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=OJ:L_202601744.