optimize-agent-prompt

# Optimize Agent Prompt Optimize a Browserbase Agent's `systemPrompt` while holding its task, result schema, variables, and evaluation criteria fixed. Treat the outer agent as the teacher and each Browserbase Agent run as an inner-agent rollout. Use Node.js 18 or later and set `BROWSERBASE_API_KEY`. The harness uses only Node.js built-in modules. ## Set up the experiment Choose a short experiment name and create an isolated workspace inside the demo or POC repository: ```bash node <skill-dir>/scripts/optimize_agent_prompt.mjs init \ --workspace ./agent-prompt-optimization/<experiment-name> \ --name <experiment-name> ``` Edit the generated files: - `task.json`: keep `task`, `resultSchema`, variables, browser settings, and evaluation oracle stable across iterations. - `prompts/iteration-001.md`: write the minimal baseline system prompt. Include irreversible-action guardrails when applicable. Use concrete success criteria. Prefer a strict JSON Schema with required fields and `null` for unavailable facts. Add known-field regexes and factuality-warning regexes under `evaluation` when a truth oracle exists. Read [references/evaluation.md](references/evaluation.md) when designing the task or score. ## Run the baseline ```bash node <skill-dir>/scripts/optimize_agent_prompt.mjs run \ --workspace ./agent-prompt-optimization/<experiment-name> \ --prompt prompts/iteration-001.md \ --label iteration-001 ``` The harness creates one reusable Browserbase Agent, updates its `systemPrompt` on later iterations, starts the run, polls messages and status, and writes: ```text runs/<label>/ ├── system-prompt.md ├── created-run.json ├── run.json ├── messages.json ├── session-logs.json └── summary.json ``` It stops a run after the configured message budget instead of paying for an unproductive spiral. Use `--max-messages`, `--timeout-ms`, `--proxies`, or `--verified` only when the task needs different values from `task.json`. ## Diagnose from observable evidence Start with the compact trajectory: ```bash node <skill-dir>/scripts/optimize_agent_prompt.mjs inspect \ --workspace ./agent-prompt-optimization/<experiment-name> \ --label iteration-001 ``` Then read `summary.json` and drill into `messages.json` at the first wrong or wasted turn. Agent messages expose ordered tool calls, tool results, errors, and final output. A `reasoning` part may contain no readable text; never require hidden chain-of-thought for the teacher loop. Read `session-logs.json` only when browser-level evidence can distinguish the cause—for example, a redirect, 403, failed request, console error, or hidden endpoint. Empty session logs can mean the Agent completed with search/fetch tools and never drove its browser. See [references/api.md](references/api.md) for endpoint shapes, pagination, result normalization, and trace caveats. ## Improve one heuristic Find the earliest consequential failure and state one counterfactual: > If the system prompt had instructed X, the Agent would have avoided Y, as shown by tool result Z. Copy the current prompt to `prompts/iteration-NNN.md` and make one attributable change. Typical improvements are: - cap retries after a repeated block or identical error; - distinguish public identifiers from private/internal IDs; - prefer search/fetch before launching a browser when interaction is unnecessary; - separate current snapshots from dated historical events; - define when a qualified fallback counts as completed; - require `null` instead of guessed values; - add a tool-call or evidence budget. Keep wins. If the new run regresses, restore the previous prompt and test a different hypothesis rather than stacking more rules. ## Judge and converge Generate the comparison table after each run: ```bash node <skill-dir>/scripts/optimize_agent_prompt.mjs report \ --workspace ./agent-prompt-optimization/<experiment-name> ``` Judge more than field completeness. Require: - terminal status `COMPLETED`; - required fields populated or explicitly nullable; - known-fact checks passing when available; - no factuality-warning match; - provenance and safety constraints preserved; - fewer messages or lower duration without quality loss. Once a prompt wins, run it again unchanged with a new label. Converge only after it passes at least two of the last three runs and one pass is an unchanged confirmation. Do not call a prompt globally optimal from one task; describe it as the best prompt for the tested task distribution. ## Graduate into the demo Use the confirmed prompt as the Agent's production `systemPrompt`. Keep the strict result schema and per-run variables. Preserve the experiment workspace or its report so reviewers can audit why each instruction exists. In the final handoff, report: - baseline versus winning score, duration, and message count; - the first wrong turn each prompt change fixed; - whether session logs added evidence; - the winning prompt path; - confirmation-run results; - limitations and the next holdout matrix.