---
title: "Best tools to measure AI agent task completion and success rate (2026)"
description: "The best tools to measure AI agent task completion in 2026 are Amplitude Agent Analytics, Datadog, Splunk Agent Observability (formerly Galileo), PostHog, and Opik, which ship built-in completion signals or judges. Agnost AI fits teams that define success with their own rules and event outcomes and want failed tasks turned into fixes. Dimension Labs covers platform support bots."
author: "Agnost AI Team"
datePublished: "2026-10-29T00:00:00.000Z"
dateModified: "2026-10-29T00:00:00.000Z"
canonical: "https://agnost.ai/blog/best-tools-measure-ai-agent-task-completion/"
tags: ["Task Completion Rate","AI Agent Success Rate","Agent Monitoring","Agent Evals","Tool Comparison"]
---

# Best tools to measure AI agent task completion and success rate (2026)

The best tools to measure AI agent task completion in 2026 are Amplitude Agent Analytics, Datadog, Splunk Agent Observability (formerly Galileo), PostHog, and Opik, which ship built-in completion signals or judges. Agnost AI fits teams that define success with their own rules and event outcomes and want failed tasks turned into fixes. Dimension Labs covers platform support bots.

A task success rate is only as good as its definition of success. Some tools ship a built-in completion signal or LLM judge; others let you send the outcome from the system that knows it. This guide compares nine tools by how they decide a task succeeded, what they analyze, and how they are priced. We make Agnost AI, so it is listed first; every other tool gets the same structure and its limitations come from its own documentation. For how to define success before you pick a tool, read [how to measure AI agent task success without inflating the number](/blog/measure-agent-task-success-rate/).

## Quick comparison


| Tool | Best for | Analyzes | Open source | Self-host | Pricing | How it measures completion |
|---|---|---|---|---|---|---|
| **Agnost AI** | Product-specific success definitions; fixes as PRs | Conversations, tool calls, traces | No | Enterprise (VPC) | Free; $49, $499/mo; per event | `success` event field, rules, tool-call success rate |
| **Amplitude Agent Analytics** | Teams already on Amplitude | Agent sessions, product events | No (SDKs only) | No (US/EU cloud) | Free; per agent session | Built-in Task Completed signal |
| **Datadog LLM Observability** | Teams already on Datadog | Traces, sessions, evals | No (SDK only) | No (SaaS) | Free tier; Pro from $160/mo; per LLM span | Goal Completeness judge per session |
| **Splunk Agent Observability (Galileo)** | Enterprises needing guardrails or on-prem | Traces, sessions, evals | No (SDK only) | Yes (VPC, on-prem) | From $100/mo per 1.2M spans (annual) | Action Completion metric per session |
| **PostHog** | Teams already on PostHog | LLM traces as product events | Yes (MIT core) | Yes (unsupported) | 100k events/mo free; per event | Task outcome judge, including handed off |
| **Opik** | Open-source, self-hosted judges | Traces, threads, evals | Yes (Apache-2.0) | Yes, free | Free; Pro $19/mo; per span | SessionCompletenessQuality judge |
| **Maxim AI** | Simulation before release, online evals after | Traces, sessions, simulations | No | Enterprise (in-VPC) | Free; $29, $49/seat/mo | Task Success evaluator |
| **Trodo** | Completion joined to product funnels | Traces, evals, product events | No | Enterprise | Free; Pro $249/mo; per unit | Task completion per job |
| **Dimension Labs** | Contact-center platform bots | Customer conversations | No (none found) | None found | From $299/mo | Containment vs. users who gave up |


## Agnost AI

**What it is.** Conversation analytics for production AI agents. Agnost AI reads real chat and voice conversations to show what users wanted, where they got stuck, and where the agent broke its own rules, then turns recurring failures into pull requests your team reviews.

**Best for.** Teams whose definition of success is specific to their product and lives in their own systems, who want each failed task traced to the turn or tool call behind it and the recurring causes fixed.

**Key features.**
- An outcome on every event: the `success` field on the [ingestion API](https://docs.agnost.ai/capture-event), or an interaction marked unsuccessful in the Python and TypeScript SDKs. Unsuccessful interactions stay visible in Events, so the code that knows the real outcome can mark a task failed.
- [Tool-call analytics](https://docs.agnost.ai/using-tool-calls): volume, success rate, error distribution, and latency per tool.
- Success contracts as [violations](https://docs.agnost.ai/using-violations): write the condition a successful conversation must meet ("confirm the reservation ID before ending the conversation") and Agnost reports the violation count against analyzed conversations, with the supporting messages.
- [Intents](https://docs.agnost.ai/using-intents) to split results by what users wanted. Agnost's docs say plainly that an intent match is not proof the user succeeded, so pair it with outcomes.
- Threshold [alerts](https://docs.agnost.ai/using-alerts) on any rule or intent.
- [Auto Improve](https://docs.agnost.ai/using-improvements) prepares a change for a recurring failure, opens a pull request, and shows outcome measurements after the change is deployed.

**Limitations.** No built-in conversation-level task-completion rate or completion judge: you define success through the `success` field or a rule, so the number is only as good as that definition. The dashboard reports success rate per tool call, not per conversation.  Not open source; self-hosting is an Enterprise option. No built-in PII redaction before ingestion ([data governance](https://docs.agnost.ai/data-governance)).

**Pricing.** Free (1,000 events a month, 7-day retention), Starter $49/month (10,000 events), Pro $499/month (1,000,000 events), Enterprise custom with self-hosted VPC.

## Amplitude Agent Analytics

**What it is.** Amplitude's product analytics plus Agent Analytics, which observes what users ask an AI agent, evaluates where it fails, and ties traces and evals to conversion, retention, and revenue.

**Best for.** Teams that already run Amplitude and want completion rates in the same funnels and cohorts as their product metrics.

**Key features.** A built-in Task Completed session signal, alongside User Intent, User Friction, Response Quality, and other signals. Custom LLM and code-based evaluators that run on all sessions (bring your own LLM key). Agent events work in existing funnels, cohorts, and retention analyses. OpenTelemetry GenAI spans flow in through a bridge.

**Limitations.** Agent sessions are capped at 5,000 a month on Free and Plus. Conversation text is kept 14 days on Free and Plus (30 days on Growth and Enterprise). Some sections are still labeled beta.

**Pricing.** Free (2M events and 5,000 agent sessions a month). Plus starts at $0 with the same agent-session allowance. Growth and Enterprise are custom. Agent Analytics is billed on agent sessions.

## Datadog LLM Observability

**What it is.** Datadog's LLM Observability, now also sold as Agent Observability: traces, evaluations, and experiments for AI agents, sharing trace IDs with APM, infrastructure, and RUM.

**Best for.** Teams already on Datadog who want completion rates next to backend root-cause analysis and SRE-grade alerting.

**Key features.** A Goal Completeness LLM-as-judge template that checks whether the agent resolved the user's intent by analyzing full session spans. Evaluators run automatically on production traffic with sampling, at span, trace, or session scope. OpenTelemetry GenAI conventions are accepted with no Datadog SDK required. Conversations map to sessions through `gen_ai.conversation.id`.

**Limitations.** Default trace retention is 15 days. Completion is judged from the conversation, not from your system of record. User-level funnels and retention need separate Datadog products.

**Pricing.** Free up to 40,000 LLM spans a month with 15-day retention. Pro starts at $160/month billed annually for 100,000 LLM spans. Billed on LLM spans only, not tool or retrieval spans.

## Splunk Agent Observability (formerly Galileo)

**What it is.** Galileo's AI observability and eval platform, now part of Splunk after Cisco's acquisition and sold as Splunk Agent Observability.

**Best for.** Enterprises, especially Splunk and Cisco shops, that want completion metrics that also run as production guardrails, with VPC or on-prem deployment.

**Key features.** An Action Completion metric that determines whether the agent accomplished all of the user's goals in a session. Luna, small distilled evaluator models that can score all production traffic. Metrics run on log streams with sampling rates and metadata rules. OpenTelemetry and OpenInference integrations.

**Limitations.** The focus is failure detection and guardrails, not user analytics: no user-level funnels or retention were found. Pricing and packaging are changing after the acquisition. Splunk's free edition covers up to 15 hosts with no time limit; span limits for agent observability on the free edition are not published. **Pricing.** Splunk lists a starting price of $100 per month per 1.2 million spans, billed annually.

## PostHog

**What it is.** PostHog's AI Observability (formerly LLM analytics) captures every LLM call as a trace built on PostHog events, so each trace carries the user, their session replay, and any exceptions.

**Best for.** Teams already on PostHog who want task outcomes next to funnels, session replay, and feature flags.

**Key features.** A Task outcome LLM-judge template that labels a trace or session resolved, partly resolved, unresolved, or handed off, plus a User correction template. Evals run per generation, trace, or whole session with 0.1% to 100% sampling. Funnels and retention on the same event data.

**Limitations.** LLM-judge evals need your own provider key, and each evaluation run counts as an event. Full prompt and response text is deleted after 30 days. Self-hosting is free but officially unsupported.

**Pricing.** First 100,000 AI events a month free, then from $0.00035 per event, falling with volume. No per-seat charges.

## Opik

**What it is.** Comet's open-source observability and evaluation platform for agents, with a library of LLM-judge metrics.

**Best for.** Teams that want Apache-2.0, self-hosted tracing with a ready-made conversation completion judge.

**Key features.** `SessionCompletenessQuality`, which judges whether the user's top-level goals were met across a thread, plus thread metrics for frustration, coherence, and knowledge retention. Online evaluation rules at trace, thread, and span scope with sampling and filters; thread rules wait for a cooldown (15 minutes by default) before judging.

**Limitations.** Completion is an evaluator you configure as a rule, and rules only run on data logged after you create them. There is no per-user view; dashboards group trace metrics by tags, a metadata key, name, or error. **Pricing.** Self-hosted open source is free. Cloud Free covers 25,000 spans a month; Pro is $19/month for 100,000 spans, then $5 per 100,000.

## Maxim AI

**What it is.** An evaluation and observability platform to simulate, evaluate, and observe AI agents. Its homepage now leads with the Bifrost gateway; the eval platform lives at getmaxim.ai/evals.

**Best for.** Teams whose main need is pre-release testing, multi-turn simulation and offline evals, feeding the same evaluators in production.

**Key features.** A Task Success evaluator that checks whether the agent accomplished the intended goal of a task. Online evals at session, trace, and span level with filters and sampling. AI-powered multi-turn simulation across scenarios. OTLP ingestion.

**Limitations.** Online evals require the Professional tier or higher, and custom dashboards the Business tier. Free-tier retention is 3 days.

**Pricing.** Developer free (up to 3 seats, 10,000 logs a month). Professional $29 per seat per month (100,000 logs). Business $49 per seat per month (500,000 logs). Enterprise custom, with in-VPC deployment.

## Trodo

**What it is.** Agent evaluation and observability that checks every trace, groups failures into issues with proposed fixes, and joins agent traces to product analytics.

**Best for.** Teams that already emit OpenTelemetry and want completion per job connected to product funnels in one tool.

**Key features.** Job discovery groups traces by what people asked the agent to do, with volume, satisfaction, task completion, and failure rate per job. Slack alerts when a job's failure rate or cost crosses a threshold. Funnels link a drop to the conversations behind it.

**Limitations.** How task completion is decided per job is not described on the pages we checked. Conversation-level frustration detection is not mentioned. Self-hosting is Enterprise-only.

**Pricing.** Developer free (10,000 units, 1,000 executions a month). Pro $249/month or $2,499/year. 14-day Pro trial.

## Dimension Labs

**What it is.** An analytics platform for customer conversations, reviews, surveys, and calls that writes structured fields back to each record and ranks drivers by their effect on outcomes. The former Dashbot domain (dashbot.io) now redirects to Dimension Labs.

**Best for.** CX and contact-center teams running platform bots such as Ada, Cognigy, Dialogflow CX, Copilot Studio, or Intercom.

**Key features.** Containment, escalation, and task-completion measurement, with a Bot Containment view that separates real containment from customers who gave up. Failure-mode fields such as loops and repeated answers. Journey drop-off and causal driver analysis.

**Limitations.** No trace, tool-call, or OpenTelemetry ingestion is mentioned, so it fits platform bots better than custom agents. No free tier.

**Pricing.** Explore $299/month, Discover $999/month, Prove $2,999/month, Scale $6,999/month, Enterprise from $150,000/year.

## What a completion number should include

Whatever tool you pick, a success rate needs a written success contract per task type, an eligible population, a fixed observation window, and four outcome states: confirmed success, confirmed failure, pending, and unknown. Reporting only the successes among confirmed outcomes hides every task the system could not classify. The [task success methodology guide](/blog/measure-agent-task-success-rate/) covers each of these in detail.

Two cautions apply to every tool above. First, an LLM judge reads the conversation, so it can mark a task complete when the agent only claimed completion; join actions to the system that owns them, as described in [detecting when an agent claims success without acting](/blog/detect-ai-agent-false-success/). Second, completion is not the whole experience: a task can finish after five rephrasings and a frustrated user, which is why [success rate alone says little about agent experience](/blog/agent-success-rate-vs-agent-experience/).

## How to choose

| If your team… | Start with |
|---|---|
| Defines success in its own systems and wants failed tasks traced and fixed as PRs | Agnost AI |
| Already runs Amplitude and wants completion in its funnels and retention charts | Amplitude Agent Analytics |
| Already runs Datadog APM | Datadog LLM Observability |
| Needs completion metrics that double as guardrails, with on-prem deployment | Splunk Agent Observability |
| Already runs PostHog and wants outcomes next to session replay | PostHog |
| Needs a free, self-hosted completion judge | Opik, or Arize Phoenix's Completeness evaluator |
| Tests agents in simulation before release | Maxim AI |
| Wants completion per job joined to product funnels | Trodo |
| Runs a contact-center bot on a platform like Ada or Dialogflow | Dimension Labs |
| Builds on LangChain or LangGraph | LangSmith, with a custom multi-turn online evaluator for outcome (no prebuilt completion evaluator) |
| Already traces with Langfuse | Langfuse, recording a resolved score from reviewers, feedback, or your own judge |

Many teams use two measures: a judge-based completion score for conversations with no system of record, and authoritative outcomes sent from code for actions such as refunds, bookings, and exports. Track [cost per completed task](/blog/ai-agent-cost-per-completed-task/) once the completion number is trustworthy.

## Related reading

- [How to measure AI agent task success without inflating the number](/blog/measure-agent-task-success-rate/), the companion guide on defining success
- [Best conversation analytics tools for AI agents (2026)](/blog/best-conversation-analytics-tools-ai-agents/)
- [Why your agent's success rate tells you nothing about agent experience](/blog/agent-success-rate-vs-agent-experience/)
- [Best tools to detect user frustration in chatbots and AI agents (2026)](/blog/chatbot-frustration-detection-tools/)
- [How to detect when an AI agent claims success without taking action](/blog/detect-ai-agent-false-success/)

## Frequently asked questions

### How do I measure task completion for my AI agent?

Write a success contract for each task type, record the outcome from the system that can verify it, and count tasks rather than messages. Keep pending and unknown outcomes separate from failures. Where no system of record exists, an LLM judge such as Datadog Goal Completeness, PostHog Task outcome, or Opik SessionCompletenessQuality can score whole conversations, but calibrate it on a labeled sample first.

### Can an LLM judge tell whether my agent completed the task?

Partly. A judge can read a conversation and decide whether the user's goal looks resolved, which works well for questions and advice. It cannot see whether a refund was issued or a booking exists, so for actions you need the outcome from the system that owns them. Use the judge where that system does not exist and check it against human labels.

### What is the difference between task completion and tool-call success rate?

Tool-call success rate counts calls that returned without an error. Task completion asks whether the user got what they came for. A conversation can have every tool call succeed and still fail the user, and a task can succeed after one failed call that the agent retried, so track both.

### What is a good task success rate for an AI agent?

There is no universal benchmark, because the number depends entirely on how you define success, which tasks count, and how long you wait for an outcome. Compare your rate against your own baseline under the same definition, and publish the denominator and the share of tasks with unknown outcomes next to it.

### Is there an open-source tool to measure agent task completion?

Opik is Apache-2.0 and ships a SessionCompletenessQuality judge that scores whether the user's top-level goals were met, and it is free to self-host. Arize Phoenix (Elastic License 2.0) has a Completeness evaluator. Langfuse (MIT core) has no built-in completion metric, but you can record a resolved score from reviewers, feedback, or your own judge.

### Does Agnost AI measure task success rate?

Not as a single built-in score. You define success yourself, either by sending the success field on events from your code or by writing a rule that a successful conversation must satisfy. Agnost then reports tool-call success rate, rule violation rates with evidence, and the measured outcome of each Auto Improve change after deployment.