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○Lesson 361The Full Agent Landscape in 2026
○Lesson 362Tool Use at the API Level: The Primitive
○Lesson 363MCP Deep Dive: The USB-C for AI Tools
○Lesson 364Multi-Agent Orchestration: Planner + Executor + Verifier
○Lesson 365Building with LangGraph
○Lesson 366Claude Code CLI as an Agent Platform
○Lesson 367Computer Use API: Letting AI Click Through GUIs
○Lesson 368Browser Agents: Capabilities and Pitfalls
○Lesson 369Evaluating Agent Performance: SWE-bench, WebArena, GAIA
○Lesson 370Production Agent Patterns: Queues, Retries, Idempotency
○Lesson 371Red-Teaming Agents: Injection, Escalation, Exfil
○Lesson 372Capstone: Build and Ship a Real Agent
○Lesson 421Claude Skills — reusable specialized agents
○Lesson 431ChatGPT Agents — OpenAI's Operator, matured
○Lesson 1205Parallel Codex Workflows Without Collisions
○Lesson 1208OpenAI Tool Use: Functions, Web Search, Files, MCP, Shell, and Computer Use
○Lesson 1600Personal Study Agent
○Lesson 1615Building Your First Agentic Workflow
○Lesson 1620Hermes Agent Build Lab: Map the Product
○Lesson 1621Build a Terminal Command Surface Like Hermes
○Lesson 1622Profiles and Config: Let One Agent Have Many Homes
○Lesson 1623Provider Routing: Switch Models Without Rewriting the App
○Lesson 1624Tool Registries and Permissioned Toolsets
○Lesson 1625Skills as Procedural Memory
○Lesson 1626Memory Context Fences: Recall Without Injection
○Lesson 1627Context Compression Engines
○Lesson 1628Gateway Sessions Across Discord, Slack, and CLI
○Lesson 1629Add a Messaging Platform Adapter
○Lesson 1630Delivery Routing for Cron and Agent Outputs
○Lesson 1631Cron Automations and Silent Monitors
○Lesson 1632Webhook Routines and API-Triggered Agents
○Lesson 1633Remote-Control Relay With MCP and Approval Gates
○Lesson 1634Vercel, Supabase, and Resend as a Hermes Control Plane
○Lesson 1635Agent Lab: A Queue UI for AI Work
○Lesson 1636Telemetry Dashboards for Agent Activity
○Lesson 1637Rate Limits and Cost Guards for Multi-Model Agents
○Lesson 1638Redaction and Audit Logs for Agent Systems
○Lesson 1639Evaluation and Regression Tests for Hermes Workflows
○Lesson 22100Agent Tool Permission Design: Least Privilege for Autonomous Systems
Lesson 22101Agent Evaluation Harnesses: Beyond Unit Tests for Multi-Step Behaviors
○Lesson 22102Agent-to-Human Handoffs: Designing the Escalation Path
○Lesson 22103Multi-Agent Coordination Patterns: Orchestration vs Choreography
○Lesson 22104Agent-Specific Prompt Injection Defenses: Why Standard LLM Defenses Aren't Enough
○Lesson 25400Agent Rate Limit Handling: Production-Grade Backoff and Recovery
○Lesson 25401Agent Cost Monitoring: Catching Runaway Loops Before the Bill
○Lesson 25402Agent State Management: Scaling Beyond In-Memory
○Lesson 25403Agent Permission Revocation: When Trust Breaks
○Lesson 25404Agent Debugging: Tracing What Went Wrong Across Many Steps
○Lesson 26700Agent Context Window Management: Long-Running Agents
○Lesson 26701Multi-Tool Coordination: When Agents Use 20+ Tools
○Lesson 26702Async Task Handoff: Agents That Wait for External Events
○Lesson 26703Agent Budget vs Quality: The Production Trade-off
○Lesson 26704Agent Personality and User Trust
○Lesson 27401Agent Quality Evaluation: Beyond Single-Step Accuracy
○Lesson 27402Agent Self-Correction Loops: When to Use, When to Skip
○Lesson 27403Agent Fallback Strategies: Graceful Degradation
○Lesson 27404A/B Testing Agents in Production
○Lesson 28600Agent Multi-Language Support: Beyond English-Only
○Lesson 28601Agent Edge Case Handling: When the Happy Path Breaks
○Lesson 28602Agent Cost Attribution: Who Pays for What
○Lesson 28603Agent On-Call Rotation: Who Wakes Up When Agents Fail
○Lesson 28604Agent Version Management: Coordinated Updates
○Lesson 29201Agent Cost Circuit Breakers: Preventing Runaway Bills
○Lesson 29202Agent Task Decomposition: Breaking Big Tasks Into Steps
○Lesson 29203Agent User Feedback Loops: Production Signals
○Lesson 29204Agent Data Privacy Design: User Trust as Foundation
○Lesson 31800Agent Deployment Checklist: Pre-Launch Discipline
○Lesson 31802Agent Incident Classification
○Lesson 31803Detecting Novel Agent Failure Modes
○Lesson 31804Evaluating Multi-Step Agent Quality
○Lesson 32700Agent Error Budgets
○Lesson 32701Cross-Functional Agent Deployment Coordination
○Lesson 32702Agent Platforms vs Bespoke Builds
○Lesson 32703Team Structures for Agent Engineering
○Lesson 32704Integrating Customer Feedback Into Agent Iteration
○Lesson 34000Agent Handoff Protocols Across Vendors
○Lesson 34001Data Classification for Agent Access
○Lesson 34002Canary Deployments for Agent Updates
○Lesson 34003Feature Flag Management for Agents
○Lesson 34004Cost Anomaly Detection for Agents
○Lesson 34800Agent Engineering Team Skills
○Lesson 34801Organizational Design for Agent Engineering
○Lesson 34802Building Internal Agent Platform
○Lesson 34803Agent-Specific Incident Runbooks
○Lesson 34804Multi-Region Agent Deployment
○Lesson 35800Letting an Agent Discover Tools at Runtime (and the Risks)
○Lesson 35801Building a Budget-Aware Agent Planner
○Lesson 35802Deprecating an Agent Tool Without Breaking Live Workflows
○Lesson 35804Designing Agents That Fail Gracefully When a Tool Breaks
○Lesson 35805Agent Memory vs. Context: When to Persist and When to Re-Fetch
○Lesson 35807Designing Confirmation Prompts for Destructive Agent Actions
○Lesson 35808Replaying Agent Runs for Debugging and Regression Testing
○Lesson 35809Setting Context-Window Budget Policies for Long-Running Agents
○Lesson 37300Shadow-Mode Deployment for AI Agents
○Lesson 37301Tool Discovery Strategies for Long-Lived Agents
○Lesson 37302Checkpointing and Recovery in Multi-Step Agents
○Lesson 37303Confidence Thresholds and Human Escalation in Agents
○Lesson 37304Multi-Tenant Isolation for Customer-Facing Agents
○Lesson 37305Budget-Aware Planning for Token-Constrained Agents
○Lesson 37306Replay and Time-Travel Debugging for Agents
○Lesson 37307Emergency Stop and Kill-Switch Design for Agents
○Lesson 37308Policy-as-Code for Agent Permissions
○Lesson 37309Cross-Region Failover for Production Agents
○Lesson 38800Cross-Provider Rate Limit Orchestration for AI Agents
○Lesson 38801Prompt Snapshot Versioning for Reproducible Agent Runs
○Lesson 38802Mid-Conversation Agent-to-Agent Handoff Design
○Lesson 38803Tool Result Truncation Strategies for Agent Loops
○Lesson 38804Deterministic Replay With Tool Mocks for Agent Tests
○Lesson 38805Output Watermarking and Provenance for Agent Actions
○Lesson 38807Runaway Loop Detection for Long-Running Agents
○Lesson 38808PII Redaction Pipelines for Agent Inputs and Logs
○Lesson 38809Progressive Trust Models for Newly Deployed Agents
○Lesson 40300Setting Per-Action Cost Budgets for AI Agents
○Lesson 40301Scoping Blast Radius When You Give Agents Write Access
○Lesson 40303Designing Escalation Thresholds for Autonomous Agents
○Lesson 40304Sanitizing Untrusted Input Before Agents Touch It
○Lesson 40305Handling Knowledge Cutoff Inside Long-Running Agents
○Lesson 40306Enforcing Output Schemas on Agent Final Answers
○Lesson 40307Designing Confirmation Flows for Agent Side Effects
○Lesson 40309Setting Retention Policies for Agent Traces
○Lesson 42200Designing cold-start warmups for production AI agents
○Lesson 42201Building a just-in-time permission elevation flow for AI agents
○Lesson 42202Multi-region failover for an agent platform that calls Claude and GPT
○Lesson 42203Canary rollouts for new agent prompts and tools
○Lesson 42204Prompt caching strategy for high-traffic Claude agents
○Lesson 42205Setting concurrent tool-call limits for an AI agent
○Lesson 42206Deterministic replay tests for non-deterministic AI agents
○Lesson 42207Customer data isolation patterns for multi-tenant AI agents
○Lesson 42208Validating AI agent output against a Zod or Pydantic schema
○Lesson 42209Building a dry-run mode for AI agents that touch production
○Lesson 44101AI agents and tool circuit breakers
○Lesson 44102AI agents and memory eviction policies
○Lesson 44103AI agents and per-task budget cap enforcement
○Lesson 44104AI agents and human handoff protocols
○Lesson 44105AI agents and tool schema versioning
○Lesson 44106AI agents and concurrent task limits
○Lesson 44108AI agents and PII scrubbing in outputs
○Lesson 44109AI agents and cold-start prewarming
○Lesson 46100Agentic AI: designing the tool allowlist that bounds the agent
○Lesson 46101Agentic AI: loop budgets that prevent runaway agents
○Lesson 46102Agentic AI: state vs context — what to write down
○Lesson 46103Agentic AI: human-in-the-loop gates that don't slow you down
○Lesson 46104Agentic AI: the failure-mode catalog every team needs
○Lesson 46105Agentic AI: separating planner and executor for clarity
○Lesson 46106Agentic AI: building an eval harness before scaling the agent
○Lesson 46109Agentic AI: rollouts, kill switches, and incident playbooks
○Lesson 48100Agentic AI: Set Tool-Call Budgets That Prevent Runaway Loops
○Lesson 48102Agentic AI: Choose Short-Term vs Long-Term Memory Without Building Both
○Lesson 48103Agentic AI: Pick a Multi-Agent Pattern (Or Decide You Need One Agent)
○Lesson 48104Agentic AI: Build Evals That Catch Loop and Tool-Misuse Failures
○Lesson 48107Agentic AI: Design Graceful Failure Modes Users Actually Forgive
○Lesson 48108Agentic AI: Roll Out a New Agent in Shadow Mode Before Letting It Act
○Lesson 48109Agentic AI: Write Tool Descriptions That Agents Use Correctly
○Lesson 50100AI and agent tool allowlist design
○Lesson 50101AI and agent stop conditions
○Lesson 50102AI and multi-agent handoff protocol
○Lesson 50103AI and agent action logging
○Lesson 50104AI and evals for agentic workflows
○Lesson 50105AI and agent failure mode catalog
○Lesson 50107AI and tool result validation
○Lesson 50108AI and headless browser agent safety
○Lesson 50109AI and agent retry and backoff strategy
○Lesson 56101Giving Agents a Scratchpad They Re-Read
○Lesson 56103Adding Human-in-the-Loop Checkpoints to Your Agent
○Lesson 56104Naming Agent Tools So the Model Picks the Right One
○Lesson 56107Designing Error Messages Your Agent Can Actually Use
○Lesson 56109Logging Agent Runs So You Can Debug Them Later
○Lesson 60700AI Agentic Tool-Use Failure Modes: When Function Calls Go Sideways
○Lesson 60701AI Agentic Planning and Task Decomposition Strategies
○Lesson 60702AI Agentic Memory Systems: Short-Term, Long-Term, and Episodic
○Lesson 60703AI Multi-Agent Orchestration Patterns: Supervisors, Swarms, and Pipelines
○Lesson 60704AI Agentic Browser Automation: When Vision-Plus-Action Agents Break
○Lesson 60705AI Agent Evaluation Harnesses: Beyond Pass/Fail
○Lesson 60706AI Agentic Cost Control: Token Budgets and Circuit Breakers
○Lesson 60707AI Human-in-the-Loop Agent Design: Escalation and Approval Patterns
○Lesson 60708AI Agentic RAG: Retrieval Pipelines That Actually Help Agents
○Lesson 60711AI Agent Observability: Tracing, Spans, and Replay Debugging
○Lesson 60712AI Agent Tool Design: APIs Built for LLM Consumers
○Lesson 60713AI Agent Self-Reflection: Critique Loops That Actually Improve Output
○Lesson 60714AI Agent Deployment Modes: Sync, Async, Streaming, and Batch
○Lesson 60716AI Agent Failure Recovery: Retries, Fallbacks, and Graceful Degradation

Curriculum
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Creators
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Agentic AI
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Agent Evaluation Harnesses: Beyond Unit Tests for Multi-Step Behaviors

Lesson 968 of 2116

Agent Evaluation Harnesses: Beyond Unit Tests for Multi-Step Behaviors

Agent behaviors emerge from multi-step interactions; unit tests on individual tools miss the failures that matter. Real evaluation requires task-completion harnesses with tracing and human review.

CreatorsAgentic AI~7 min readBI2 · Representation & ReasoningBI3 · LearningBI4 · Natural InteractionPrint / PDF

Big idea

Agent behaviors emerge from multi-step interactions; unit tests on individual tools miss the failures that matter. Real evaluation requires task-completion harnesses with tracing and human review.

Lesson map

What this lesson covers

11 min11 blocks5 concepts

Learning path

The main moves in order

1The premise
2agent evaluation
3task completion
4tracing

Concept cluster

Terms to connect while reading

agent evaluationtask completiontracingtrajectory analysisregression testing

Read1

Sections3

Lists2

Notes4

Terms1

Section 1

The premise

Agent quality emerges across trajectories, not within individual steps; evaluation must span trajectories.

What AI does well here

Build task-completion harnesses with realistic input distribution and ground-truth outcomes
Implement tracing that captures the full trajectory (prompts, tool calls, tool outputs, model decisions)
Use trajectory analysis to identify common failure modes (loops, premature stopping, wrong tool selection)
Maintain regression test suites that catch behavior degradation across model updates

Agent eval harness design

Design an evaluation harness for [agent]. Cover: (1) task set composition (realistic input distribution + adversarial cases + edge cases), (2) ground-truth methodology (how do we know if the agent succeeded), (3) trajectory tracing (what we capture for analysis), (4) failure-mode taxonomy (loops, premature stopping, wrong-tool selection, hallucinated tool args), (5) regression suite for new model versions, (6) the human-review subset and cadence.

Check-in 1. Got it so far?

What AI cannot do

Substitute for human review on high-stakes trajectories
Catch novel failure modes the test set doesn't cover
Replace production monitoring (eval-set performance ≠ production performance)

Evals don't predict production

Evaluation set performance gives confidence — it doesn't predict production. Production traffic distribution drifts, edge cases appear that the eval set never imagined. Pair eval harness with production monitoring.

Key terms in this lesson

agent evaluation
task completion
tracing
trajectory analysis
regression testing

Check-in 2. Got it so far?

Scope your agents tightly

Always define: goal, tools, permissions, and stop condition before executing. An unscoped agent with write access is a liability, not a helper.

Lesson complete

You've completed "Agent Evaluation Harnesses: Beyond Unit Tests for Multi-Step Behaviors". Mark this lesson done and keep going — every lesson builds on the last.

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Related lessons

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Creators · 11 min
Agent Quality Evaluation: Beyond Single-Step Accuracy
Single-step accuracy doesn't measure agent quality. Trajectory quality, task-completion rate, and human-judgment matching do.
Creators · 40 min
Replaying Agent Runs for Debugging and Regression Testing
Build a replay harness that re-runs a recorded trace against a new prompt or model.
Creators · 11 min
Setting Retention Policies for Agent Traces
Decide how long to keep agent traces, which fields to redact, and how to satisfy deletion requests.

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