The 5 Signals That Define a Good Agent Experience (And How to Measure Each One)
Here’s a scenario I see constantly. A team ships an autonomous agent, maybe a coding assistant, maybe a workflow automation tool, maybe a customer-facing support agent. Usage looks fine. Session counts are up. Users are returning. The team feels good about it.
Then someone actually looks at what’s happening inside the conversations.
The agent is completing simple tasks but failing at complex ones. Users have quietly stopped asking it for anything hard. Every time it makes a mistake, the user manually corrects it and takes over. The whole product is running, but it’s running like a car with two flat tires. Technically mobile. Not actually functional.
This is the measurement trap for agent products specifically. The metrics most teams are watching, sessions, retention, token usage, don’t distinguish between an agent that’s genuinely helping and an agent that users have learned to treat like a limited tool. They tell you the user showed up. They dont tell you whether the user TRUSTS the agent.
After tracking millions of agent conversations at Agnost AI, five signals keep showing up as the ones that actually separate good agent experiences from bad ones. Not theoretical signals. Measurable, instrumentable, production-ready signals that tell you whether your agent is earning trust or quietly losing it.
^ your product dashboard while your agent is in the middle of a 14-step loop trying to accomplish something a 3-step path would have done fine
Signal 1: Task Completion Rate (and How to Measure It When You Have No Ground Truth)
Task Completion Rate (TCR) is the percentage of agent sessions where the agent actually accomplished what the user asked it to do.
Sounds simple. In practice, most teams don’t have ground truth labels for this, and that’s where the measurement usually falls apart. You can’t manually label every agent invocation. So you proxy for it.
The most reliable behavioral proxies for TCR:
- Positive completion signals. User messages that indicate success: “great, that worked,” “perfect,” “looks good,” explicit approvals. Track the rate at which sessions end with these signals vs. without them.
- Absence of correction turns. If the user follows the agent’s output with a correction or an override, that’s a failure signal. A session where the agent acts and the user accepts without modifying is directionally a completion.
- Session scope consistency. Did the user come in with a stated goal and leave having addressed it? Or did they abandon mid-task, reframe the goal, or restart from scratch? Restarts are completions with zero score.
- Downstream action completion. For workflow agents: did the downstream artifact actually get created? Did the email send? Did the PR get opened? When your agent has side effects, those side effects are your ground truth.
What does good TCR look like by category? From what we see across agent platforms, autonomous coding agents should be hitting 60-75% on well-scoped tasks. Customer support agents with a defined resolution action (refund, cancel, escalate) can get to 70-80% if the intent taxonomy is tight. Workflow automation agents doing multi-step orchestration are often in the 55-70% range and that’s actually fine if recovery is strong.
Below 50% TCR in any category is a product problem, not a model problem. And TCR above 80% on complex tasks should make you suspicious: you’re either defining completion too loosely, or the tasks aren’t actually complex.
Signal 2: Path Efficiency (Are You Paying for the Agent to Think in Circles?)
Path Efficiency measures whether the agent took a reasonable route to complete a task, or whether it wandered, backtracked, or got stuck in loops before arriving at an outcome.
This one is directly visible in your conversation and tool-call traces. The signals to look for:
Step count vs. expected complexity. Categorize incoming tasks by complexity (simple, moderate, complex) and track median step counts per category. “Change the font color” tasks that consistently take 12 tool calls have an efficiency problem. A rough baseline: simple tasks should resolve in 2-5 steps, moderate tasks in 5-10, complex multi-component tasks in 10-20. If your agents are routinely blowing past those, dig in.
Backtracking rate. An agent that calls a tool, gets a result, calls the same tool again with slightly different params, gets another result, then calls it a third time is backtracking. This is a signal the agent didn’t reason ahead. Count the percentage of tasks where the same tool or step type appears 3+ times with no forward progress between calls.
Loop detection. The clearest signal of a broken agent: it enters a state it was already in. Tool A, then Tool B, then Tool A again, with no change in the underlying state. In production, we’ve seen agents hit 20+ step sequences that are literally just two tools alternating with each other because neither produces a stable end state. These loops cost you tokens, add latency, and usually still result in task failure.
Abandoned paths. When an agent starts a sub-task and aborts it without completing it, that’s wasted steps. Count them. Some exploration is normal in complex tasks. Systematic abandon rates above 15-20% of initiated sub-tasks are a problem.
At Agnost AI, path efficiency signals are among the most reliably predictive of user trust. An agent that completes tasks in fewer steps than expected builds trust faster than an agent that takes 3x the steps to get to the same outcome, even if the outcome is identical. Users can feel the spinning.
^ “the agent found a solution” and “the agent wandered through 18 steps and happened to arrive at a solution,” simultaneously, on your success rate dashboard
Signal 3: User Trust Signals (Trust Decay Is Measurable Before Users Tell You About It)
This is the signal most teams completely miss, and it’s the most consequential one.
User trust in an agent isn’t a feeling. It’s a behavior. And behaviors leave traces in your data.
The core trust signal: after initial interactions with your agent, are users delegating MORE to it over time, or are they taking things back into their own hands?
Specific things to instrument:
Override rate. When the agent takes an action or produces an output, does the user accept it or modify it? An override isn’t always a failure, but a rising override rate over a user’s first 2-4 weeks is a direct measurement of trust declining. Track this per user, not just in aggregate.
Manual fallback rate. For workflow agents: after an agent handles a task type, does the user subsequently do that same task type manually (without involving the agent)? This is trust decay made explicit. The user decided the agent isn’t reliable for that task and stopped asking.
Supervision intensity. How often does the user interject mid-task vs. let the agent run to completion? Healthy trust looks like users letting agents run. Low trust looks like frequent mid-task interruptions: “wait, stop,” “hold on, not that,” “actually go back.” Track the rate of interruptive messages per task.
Re-delegation rate. After a user takes something back manually, do they eventually delegate it to the agent again? Re-delegation is a trust recovery signal. No re-delegation after a manual fallback means the user made a permanent judgment call about that task type.
The pattern we see consistently: high TCR paired with rising override rate and manual fallback means the agent is completing tasks but not in ways the user trusts. The agent is technically succeeding and experientially failing. These users churn without ever reporting a “failure” because technically, nothing failed.
Signal 4: Recovery Rate (A Good Agent Fails Gracefully)
Every agent fails sometimes. The question is what happens next.
Recovery Rate is the percentage of agent failures that the agent resolves without user intervention, either by self-correcting, trying an alternative path, or gracefully communicating the limitation and offering a next step.
A high-recovery agent that fails 30% of the time can still be a great experience. A low-recovery agent that fails 10% of the time can be catastrophic if every failure requires the user to manually step in and fix things.
To measure recovery rate you need to define what recovery actually means in your product:
Self-correction recovery. Agent detects an error in its own output, corrects it in the next turn without user prompting. This is the best kind of recovery. Track how often your agent produces an output and then autonomously follows it with a correction (vs. waiting for the user to notice).
Alternative path recovery. Primary path fails, agent switches to a valid secondary path and still achieves the goal. This requires tracking whether goal state was achieved even when the originally planned tool sequence wasn’t followed.
Graceful degradation. Agent can’t complete the task, says so clearly, explains why, and either offers a partial result or a concrete next step for the user. This is not full recovery, but its worth distinguishing from a silent failure or a confident wrong answer. Users forgive “I cant do that, but here’s what I can do” a lot more than they forgive getting bad output without warning.
Silent failure. The worst outcome: agent produces output that looks complete but isn’t, user accepts it, discovers the problem later. This is negative recovery. It’s worse than an obvious failure because it destroys trust without giving the user a chance to course-correct in the moment.
From Agnost AI’s data, agents with Recovery Rate above 60% (for failures that don’t require user intervention) retain users at roughly 2x the rate of agents below 40%. The math is intuitive: users will tolerate an agent that sometimes fails if it handles those failures gracefully. They wont tolerate one that requires them to babysit every interaction.
Signal 5: Delegation Depth (The Long-Term Retention Signal Nobody Tracks)
This is the forward-looking signal. The one that tells you not just whether your agent is working today, but whether users are betting MORE on it over time.
Delegation Depth measures the complexity and ambiguity of the tasks users bring to the agent, and whether that’s trending upward or downward over time.
An agent that earns trust gets harder tasks. An agent that loses trust gets simpler tasks or no tasks.
How to measure it:
Task complexity distribution, longitudinal. For each user, track the distribution of task complexity across their sessions over time. Is the median complexity of tasks they delegate going up, flat, or down? You can proxy complexity with: task description length, number of constraints specified, number of tool calls required, multi-step vs. single-step nature of the task.
Autonomy level progression. Some agent products have explicit autonomy tiers: “ask before every action” vs. “ask before major actions” vs. “act autonomously.” Watch which direction users move over their first 60 days. Movement toward higher autonomy tiers is a direct behavioral expression of trust. Movement toward lower autonomy tiers, or failure to progress, is a trust stagnation signal.
Task category expansion. New users typically start with one or two task types where they test the agent. As trust builds, they expand to adjacent task types. Track the number of distinct task categories per user and how that changes over their first 30, 60, 90 days. Healthy agents see category expansion. Stagnant or contracting category breadth means users have drawn a hard line around what they’ll delegate.
High-stakes task rate. Define what “high-stakes” means in your domain: irreversible actions, customer-facing outputs, financial decisions, public commits. Track what percentage of tasks are high-stakes, and whether that percentage is growing for established users. Users who start giving agents high-stakes tasks have genuinely transferred trust. Users who never get there haven’t.
^ founders when they run the delegation depth analysis and realize their “retained” users are only trusting the agent with their easiest tasks
How These Signals Compound (This Is the Part Most Teams Miss)
Here’s the insight that took us a while to articulate clearly: these five signals don’t just add up, they interact.
The specific combination that should worry you most: high Task Completion Rate paired with low Delegation Depth and rising Override Rate.
What that combination means: your agent is technically completing the tasks users give it, but users have learned to only give it easy tasks, and they’re modifying its outputs even on those. The agent is passing its own test while quietly failing the user’s test.
This pattern looks fine in your retention dashboard. Users are coming back. They’re “completing sessions.” But they’ve boxed the agent into a narrow, shallow set of use cases and don’t trust it enough to go further. Growth in this state is a ceiling, not a trajectory.
The healthy version of the compound signal: TCR is 65%+, Path Efficiency is trending better over time, Override Rate is declining (users accepting outputs more), Recovery Rate is high enough that failures don’t cascade, and Delegation Depth is growing across your user base. Every signal improving together means the agent is earning trust at the product level, not just passing individual tasks.
One more compound pattern worth tracking separately: low Recovery Rate plus low TCR is a pure product quality failure. High Recovery Rate plus low TCR is an agent thats failing but at least communicating honestly, which is actually a healthier place to be than the inverse. Fix the failures, but respect the graceful degradation while you do.
The Practical Implementation Problem
Look, instrumenting all five of these signals from scratch is a real engineering investment.
The hardest part isn’t the individual metrics, it’s the data architecture. You need per-user time series on task complexity, per-session step traces for path efficiency, turn-level classification for override and correction detection, and goal-state resolution tracking that connects tool call outcomes to user-level intent. Most teams dont have this. They have session-level event logs and maybe a few custom events they’ve hand-instrumented.
This is exactly the gap we built Agnost AI to fill. Not another log viewer or a trace debugger, but a layer that understands agent conversations as a sequence of goal-directed actions and tracks the signals above natively across your whole user base. TCR proxies, path efficiency scoring, trust signal tracking, recovery classification. These are the core of what we surface in Agnost AI because they’re the signals that actually tell you whether your agent experience is working.
If you’re flying blind on any of these five signals right now, worth a look.
Wrapping it up
The teams building great agent products in 2026 aren’t the ones with the best models.
They’re the ones who understand that agent quality isn’t just task success, it’s earned trust expressed through delegation behavior. And that the only way to know whether you’re earning it is to measure the signals that capture it: completion, efficiency, trust, recovery, and depth.
Start with TCR and Path Efficiency. Those two will show you what’s broken right now. Then instrument Recovery Rate, because graceful failure is a feature, not a consolation prize. Then build the longitudinal signals, Trust Signals and Delegation Depth, because those are the ones that tell you where you’re actually headed.
The data is already in your conversation logs. You just have to build the structure to read it.
If you want to see what these signals look like across your own agent’s conversation data, Agnost tracks all five natively. Task completion proxies, path efficiency scoring, override rate detection, recovery classification, delegation depth trends. Check out Agnost AI at agnost.ai if you’re tired of inferring agent quality from session counts.
TL;DR: Five signals define a good agent experience: Task Completion Rate (proxy it with behavioral signals when you lack ground truth), Path Efficiency (detect loops and backtracking in your step traces), User Trust Signals (override rate and manual fallback are measurable), Recovery Rate (graceful failure retains users; silent failure destroys trust), and Delegation Depth (are users trusting the agent with harder tasks over time, or regressing?). High TCR plus low Delegation Depth is the most dangerous combination: the agent is succeeding at what users let it do, while users have quietly decided not to let it do the hard things.
Reading Time: ~10 min