Tips & Tricks
Practical habits that make agents safer, cheaper, and more useful. Short reads, long payoff.
Showing 52 of 52 tips
Cut the Context Tax: Navigate by Graph, Not Grep
Every file a coding agent reads via grep stays in context and is re-billed on every later turn. Replace blind reads with semantic graph queries to cut token cost up to 36% and catch call sites text search misses.
Tune Effort Level Per Task, Not Per Model
Frontier models now expose multiple effort levels that span 10x+ in token usage and cost. Choosing the right effort per task — not per model — is the single biggest cost lever available without changing models.
Version Your Skills, Not Just Your Prompts
Skills are code, not configuration. Treat them like any other dependency — version, test, and review changes before they ship to your agent.
Budget Your Cache Reads, Not Just Your Output
Cache-read pricing can dominate long agentic sessions. Track cache-hit economics separately from output-token costs or your agent budget will silently blow up.
Run Continuous Validation on Agent Benchmarks
Agent benchmarks rot when external dependencies change. Run continuous validation against your eval suite or your benchmark scores will silently drift from reality.
Separate Search from Retrieval in Agent Tool Design
When agents call a single "search" tool that both finds and fetches, they burn context on irrelevant results. Split into a cheap search call and a targeted fetch call to cut token waste by 60-80%.
Bookend Critical Context
Put your most important instructions at the beginning and end of the prompt. Models attend to the edges — the middle is where details get lost.
Externalize Agent Memory to the Filesystem
The filesystem is unlimited, persistent, and directly operable by the agent. Use it instead of stuffing everything into the context window.
Profile Tool Latency Separately From Model Latency
When agents are slow, most teams optimize model response time. But tool call latency — network round trips, API auth, and result serialization — is often the real bottleneck.
Benchmark Cache-Hit Rates, Not Just List Prices
A model with 90% cache-hit discounting can be cheaper than a model with half the list price — but only if your workload actually hits the cache.
Cap Your Agent's Wallet Before It Caps You
Agents with payment capability need bounded budgets, merchant allowlists, and spend alerts — or a runaway loop becomes a runaway bill.
Score the Trajectory, Not Just the Outcome
An agent that returns the right answer through wrong reasoning will fail unpredictably. Score the path — tool calls, retries, decisions — not just the final output.
Cost Your Agent's Idle Loops
Reasoning models that think before answering can burn tokens during idle reasoning loops. Measure idle cost before it surprises you.
Use Pass-k Scoring for Agent Evaluations
Single-run pass rates hide unreliability. Score your agents with pass-k (pass on all k attempts) to surface models that are only sometimes right — and would require manual checking in production.
Verify Open-Weight Claims Before Committing
Open-weight labels are marketing — check the license, the actual weights, the hardware requirements, and the evaluation provenance before building on a model.
Tag Every Agent Run With a Trace ID
If you cannot identify which run produced which output, you cannot debug, you cannot evaluate, and you cannot improve. A trace ID is the minimum metadata for any production agent.
Audit Your Benchmarks Adversarially Before Trusting Them
A benchmark that a zero-capability agent can pass is not measuring capability — it is measuring your test's bugs. Run a null agent before publishing any score.
Route by Task Difficulty, Not by Brand
The cheapest frontier tier now outscored last quarter's flagship. Stop sending every request to your most expensive model — route by difficulty.
Audit Your AI Tool Supply Chain Weekly
Malicious PyPI and NPM packages targeting AI developers are now weekly news. A 10-minute weekly audit of new dependencies catches supply chain attacks before they catch you.
Compress Context As You Go, Not After It Breaks
Summarize resolved sub-tasks and trim verbose tool outputs before the context window fills — waiting until auto-compact triggers means you already lost quality.
Isolate Sub-Task Contexts to Prevent Cross-Contamination
Give each distinct sub-task its own clean context window — one task's noise should never contaminate another's reasoning.
Pin Your Model Version in Production
Model providers silently update versions behind the same alias. Pin the exact model ID or your agent's behavior will change overnight with no code change.
Separate Inference from Orchestration in Your Agent Stack
Keep the LLM inference layer and the agent orchestration layer as independent components — swap models without rewriting logic, and swap logic without re-tuning models.
Test Against World Model Simulators Before Burning Real API Tokens
Use language world models like Qwen-AgentWorld to simulate agent environments offline — catch failure modes in mock environments before spending real API budget on live tools.
Budget Your Context Window Like a Paycheck
Context window is not free space — it is a finite resource. Allocate it deliberately or your agent will spend it on noise and starve on signal.
Log Token Cost Per Task, Not Per Day
Daily spend hides which agent workflows are expensive. Attribute tokens to named tasks so you can kill or rewrite the wasteful ones.
Prefer Cite-or-Abstain Over Confident Guessing
For research and marketing claims, force agents to cite sources or abstain. Confidence without evidence is a liability.
Separate Draft Autonomy From Send Authority
Let agents prepare freely. Hold anything that publishes, spends, deletes, or messages externally for explicit approval.
Extract Five Content Atoms From Every Pillar Post
Do not rewrite from scratch for each platform. Pull quotes, stats, tips, stories, and CTAs from one long-form piece.
Pin Verification Commands in the Repo Root
Agents finish faster and lie less when Definition of Done is an executable command block in AGENTS.md or PROGRESS.md.
Validate Outputs Before Acting
Never let an agent fire a destructive tool based on unverified output. A fast sanity check prevents irreversible mistakes.
Cache Successful Prompts
If an agent solves a task well once, turn that prompt into a reusable template instead of reinventing it every run.
Keep Prompts Under Review
Prompts are code. Put them through the same review, test, and rollback discipline as any other production artifact.
Use Structured Output for Tool Calls
Make your agent's tool calls strict, parseable, and predictable by forcing JSON schemas or structured output instead of free-form text.
Write the Prompt You Would Send a Junior Dev
If you would not hand the task to a new hire as written, the agent will also struggle. Clarity before cleverness.
Ground the Agent with Examples
One good example is worth a page of abstract instructions. Show the agent what right looks like.
Review Every Diff
Agents are fast, not infallible. The 30 seconds you spend reviewing a diff saves hours of debugging later.
Use a Sandbox First
Run agent-generated code in isolation before it touches your main project. A cheap container beats a broken production environment.
Cache Common Prompts
Save your best prompts as templates. Reuse, refine, and version them the same way you reuse code.
Version Your Prompts
A prompt is code. Code needs versioning. Track what changed and why, so you can roll back when a prompt regresses.
Ship One Thing at a Time
Agents can produce a lot of output quickly. Resist the urge to batch everything into one giant change.
Measure Before You Optimize
Don't swap models, chunkers, or databases on a hunch. Measure first. The bottleneck is rarely where you think it is.
Use Skills, Not Monolithic Agents
Compose agents from reusable skills. Small, focused capabilities are easier to test, share, and combine than one giant prompt.
Never Trust a Hallucination
Agents will confidently invent facts, URLs, APIs, and code. Verify anything that matters before you use it.
Keep a Human in the Loop
Autonomy is a dial, not a switch. Start with suggestions, move to approvals, and only automate after the agent proves itself.
Define "Done" Before You Prompt
If you cannot describe success, the agent cannot deliver it. Define the outcome before you ask for the output.
Start Small
Give your agent one focused task before asking it to refactor an entire codebase.
Watch Token Costs
A looped agent can burn tokens fast. Set budgets and use cheaper models for iteration.
Start With a Thin Prompt
Give the agent one focused task first, then expand scope once it proves itself.
Commit Often
Give your agent a clean branch and frequent commits. Rollback is your friend.
How to Pick a Local Model for Coding
Choose the right quantized model for your hardware, task, and quality bar.
When to Use Agent Mode vs. Chat
Agent mode is powerful but expensive and noisy. Know when to turn it on and when to stay in chat.