Agent Loop Best Practices

Ten habits for keeping agent loops bounded, observable, and predictable - drawn from the loop model, stop rules, reasoning, mode choice, and context discipline in this section.

How to Use This List

Use items 1-4 before first production traffic (control plane).
Use items 5-7 while designing tools and prompts (decision quality).
Use items 8-10 in operations and iteration (evidence loop).
Cross-link deeper pages when an item fails an audit; do not treat this list as a full design doc.

Treat the loop as the product, not the model alone. Ship perceive → reason → act → observe with explicit host ownership of tools and stops. If you cannot draw the back-edge and the exit, you do not have an agent design yet. See Inside the Agent Loop.
Enforce hard stops the model cannot override. Set max model turns, max tool calls, run timeouts, and cost ceilings in host code. Record a machine-readable stop_reason on every run. Soft "please finish" prompts are extras, not controls. See Stopping Conditions.
Pick an operating mode on purpose. Single-turn, multi-turn, and long-running agents need different state, budgets, and oversight. Do not run a forever process when a multi-turn session would do. See Single-Turn vs Multi-Turn vs Long-Running Agents.
Log trajectories as first-class telemetry. Persist run id, per-step decisions, tool args (redacted), result hashes, latency, tokens, and stop reason. Final answers without trajectories make runaways undebuggable. See Why Agents Loop Forever.
Define done before you define tools. Write success criteria, partial-success behavior, and user-visible failure copy. "Be thorough" is not a stop condition. Pair checklists with caps so the agent can finish.
Prefer legible observations over raw dumps. Return structured, short tool results with actionable errors. Identical retries should be blocked by host guards after a small budget, not left to hope. See Agent Loop Basics.
Manage context like a scarce budget. Pin goal and safety rules each turn, trim old payloads, and limit tool schema sprawl. What is not in the window cannot affect the next decision. See How Context Shapes Every Decision.
Match planning depth to task shape. Use reactive next-step control when observations branch heavily; use explicit plans (with replan) for multi-stage work and for pre-approving side effects. Measure tokens per successful task, not plan length vanity. See Planning vs Reacting.
Separate operator traces from user UX. Keep reasoning traces and full tool detail for logs and evals; show users progress and final answers. Traces help debugging and are not automatic trust. See Reasoning Traces.
Regression-test the loop, not only the prose. Golden tasks should assert step caps, forbidden repeat signatures, required tools, and stop reasons under stubbed tools. Raise limits only when evals prove real successes were truncated, not to silence timeouts.

FAQs

Why only ten practices?

This section summary is a tight operational set. Deeper pages expand mechanics, FAQs, and architecture choices without turning this list into a novel.

What's the single highest-ROI habit?

Hard stops plus trajectory logging. Together they bound blast radius and make every other fix measurable.

Do frameworks implement these for me?

Frameworks provide hooks and defaults. You still set limits, design tools, and define done for your product.

How often should we revisit max turns?

After eval packs change or production max_turns stop rates move. Revisit with data, not after one loud incident alone.

Are these practices language-specific?

No. Examples in this section use Python for clarity; the control ideas apply to any host runtime.

Where do security practices fit?

Here: treat tool outputs as untrusted context and keep deny rules in host code. Full security patterns live in the security group of this guide.

Should every agent be plan-and-execute?

No. Start reactive with caps; add planning when evals show waste or when writes need pre-approval.

How do these habits map to multi-agent systems?

Apply the same caps and trajectory logging per agent and add a global hop/handoff budget so peers cannot ping-pong forever.

Stack versions: Pins from the category manifest (verify at build): OpenRouter (~315+ models, July 2026 pricing/fees); LangGraph 1.0+; CrewAI 1.14+; Microsoft Agent Framework 1.0; Vercel AI SDK 6; Pydantic AI (latest); LlamaIndex (latest); OpenAI Agents SDK (latest + MCP); MCP (Linux Foundation governance); A2A (HTTP+SSE+JSON-RPC 2.0); Solana @solana/web3.js + @solana/spl-token.

How to Use This List

Use items 1-4 before first production traffic (control plane).

Use items 5-7 while designing tools and prompts (decision quality).

Use items 8-10 in operations and iteration (evidence loop).

Cross-link deeper pages when an item fails an audit; do not treat this list as a full design doc.

Treat the loop as the product, not the model alone. Ship perceive → reason → act → observe with explicit host ownership of tools and stops. If you cannot draw the back-edge and the exit, you do not have an agent design yet. See Inside the Agent Loop.

Enforce hard stops the model cannot override. Set max model turns, max tool calls, run timeouts, and cost ceilings in host code. Record a machine-readable stop_reason on every run. Soft "please finish" prompts are extras, not controls. See Stopping Conditions.

Pick an operating mode on purpose. Single-turn, multi-turn, and long-running agents need different state, budgets, and oversight. Do not run a forever process when a multi-turn session would do. See Single-Turn vs Multi-Turn vs Long-Running Agents.

Log trajectories as first-class telemetry. Persist run id, per-step decisions, tool args (redacted), result hashes, latency, tokens, and stop reason. Final answers without trajectories make runaways undebuggable. See Why Agents Loop Forever.

Define done before you define tools. Write success criteria, partial-success behavior, and user-visible failure copy. "Be thorough" is not a stop condition. Pair checklists with caps so the agent can finish.

Prefer legible observations over raw dumps. Return structured, short tool results with actionable errors. Identical retries should be blocked by host guards after a small budget, not left to hope. See Agent Loop Basics.

Manage context like a scarce budget. Pin goal and safety rules each turn, trim old payloads, and limit tool schema sprawl. What is not in the window cannot affect the next decision. See How Context Shapes Every Decision.

Match planning depth to task shape. Use reactive next-step control when observations branch heavily; use explicit plans (with replan) for multi-stage work and for pre-approving side effects. Measure tokens per successful task, not plan length vanity. See Planning vs Reacting.

Separate operator traces from user UX. Keep reasoning traces and full tool detail for logs and evals; show users progress and final answers. Traces help debugging and are not automatic trust. See Reasoning Traces.

Regression-test the loop, not only the prose. Golden tasks should assert step caps, forbidden repeat signatures, required tools, and stop reasons under stubbed tools. Raise limits only when evals prove real successes were truncated, not to silence timeouts.

FAQs

Why only ten practices?

This section summary is a tight operational set. Deeper pages expand mechanics, FAQs, and architecture choices without turning this list into a novel.

What's the single highest-ROI habit?

Hard stops plus trajectory logging. Together they bound blast radius and make every other fix measurable.

Do frameworks implement these for me?

Frameworks provide hooks and defaults. You still set limits, design tools, and define done for your product.

How often should we revisit max turns?

After eval packs change or production max_turns stop rates move. Revisit with data, not after one loud incident alone.

Are these practices language-specific?

No. Examples in this section use Python for clarity; the control ideas apply to any host runtime.

Where do security practices fit?

Here: treat tool outputs as untrusted context and keep deny rules in host code. Full security patterns live in the security group of this guide.

Should every agent be plan-and-execute?

No. Start reactive with caps; add planning when evals show waste or when writes need pre-approval.

How do these habits map to multi-agent systems?

Apply the same caps and trajectory logging per agent and add a global hop/handoff budget so peers cannot ping-pong forever.

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