Retrieve tools: Dynamically surface only relevant tools per query instead of all 40.

Write clear specs: Give precise names, descriptions, and argument schemas.

Group by domain: Namespace or cluster tools to reduce confusion.

Add few-shot examples: Show correct tool-use patterns.

Validate calls: Schema-check arguments and reject invalid invocations.

Handle overlap: Disambiguate similar tools with distinct descriptions.

Fail gracefully: Recover when the wrong tool is chosen or errors out.

Monitor selection: Track per-tool accuracy and misfire rates.

Control randomness: Fix temperature/seeds where the provider supports it.

Pin versions: Lock model, prompt, and config for each deployment.

Snapshot context: Store retrieved data and inputs to replay exact runs.

Isolate non-determinism: Separate deterministic logic from generative steps.

Test with fixtures: Use recorded model responses for unit tests.

Assert on structure: Validate output shape/constraints, not exact strings.

Log full traces: Capture everything needed to reproduce any decision.

Track variance: Measure output stability across repeated runs.

Detect escalation triggers: Low confidence, sensitive topics, repeated failure, or explicit requests.

Preserve context: Pass full conversation history and state to the human agent.

Summarize for the human: Provide a concise brief so they don't re-read everything.

Route intelligently: Send to the right team based on issue type and priority.

Set clear boundaries: Define what the agent can and can't handle autonomously.

Support takeover: Let humans seamlessly assume and later return control.

Capture outcomes: Log resolutions to improve future automation.

Measure handoff quality: Track escalation rate, resolution time, and satisfaction.

Enforce schemas: Use structured-output/function-calling modes or JSON schema constraints.

Validate on receipt: Parse and schema-check every response before using it.

Retry on failure: Re-prompt with the error when output is malformed.

Use constrained decoding: Apply grammars/regex to force valid structure where supported.

Give clear examples: Show exact target format in the prompt.

Isolate reasoning: Keep free-form thinking out of the structured field.

Add fallbacks: Repair partial output or degrade gracefully on repeated failure.

Monitor validity: Track format-error rates as a health metric.

Minimize data: Send only what's necessary; strip irrelevant fields.

Redact PII: Detect and mask sensitive data before it reaches the model.

Control residency: Route data to compliant regions and providers.

Enforce retention: Set deletion policies and honor no-training agreements.

Encrypt everywhere: Protect data in transit and at rest.

Gate access: Least-privilege, audited access to user data.

Get consent: Track user permissions and support data-deletion requests.

Audit and log: Maintain records to demonstrate compliance.

Use structured messages: Pass typed schemas/JSON instead of free-form text.

Define clear contracts: Specify each agent's inputs, outputs, and responsibilities.

Minimize payloads: Send references/IDs, not full context, to cut tokens.

Add a coordinator: Use a supervisor to route work and resolve conflicts.

Share state externally: Use a common store instead of relaying everything in messages.

Handle failures: Timeouts, retries, and fallbacks when an agent stalls.

Prevent loops: Cap hops and detect circular delegation.

Trace end-to-end: Log every inter-agent message with a correlation ID.

Normalize ingestion: Convert every source into a consistent, chunked, metadata-tagged format.

Tag by source/type: Attach origin, freshness, and access level to each chunk.

Route queries: Detect which source(s) a question needs before retrieving.

Merge and rerank: Combine results across sources, then rerank for relevance.

Respect permissions: Filter retrieval by the user's access rights.

Prefer authoritative sources: Weight canonical docs over stale or duplicate ones.

Cite precisely: Point to exact source, section, and version.

Monitor per-source quality: Track which sources drive good vs. bad answers.

Separate plan from act: Generate an explicit plan before executing steps.

Decompose tasks: Break complex goals into ordered, verifiable subtasks.

Track state: Maintain progress so the agent knows what's done and what's next.

Verify each step: Check intermediate results before proceeding.

Allow replanning: Let the agent adapt when a step fails or new info appears.

Bound execution: Cap steps/iterations to prevent runaway loops.

Use the right pattern: Apply ReAct, plan-execute, or a supervisor as the task demands.

Log the trace: Record the full plan and execution for debugging.

Stream end-to-end: Pipeline STT → LLM → TTS with streaming at every stage.

Cut first-token latency: Start generating and speaking before full reasoning completes.

Detect turns: Use robust endpointing and barge-in so users can interrupt.

Chunk TTS: Synthesize speech incrementally as tokens arrive.

Use fast models: Prefer low-latency models for conversational turns.

Parallelize: Run retrieval/tools concurrently with generation.

Handle noise: Manage silence, filler, and disfluencies gracefully.

Fallback fast: Recover smoothly from STT errors or timeouts.

Use batch APIs: Exploit provider batch pricing for non-urgent throughput.

Parallelize with limits: Scale concurrency while respecting rate limits.

Checkpoint progress: Persist state so failures resume instead of restarting.

Make it idempotent: Ensure reprocessing doesn't duplicate work or side effects.

Right-size models: Route each job to the cheapest model that meets quality.

Cache and dedupe: Skip identical requests already processed.

Handle partial failures: Retry failed items independently, quarantine bad inputs.

Monitor throughput: Track cost, completion rate, and per-item latency.

Modularize prompts: Break into reusable, composable components (system, task, examples).

Version control: Treat prompts as code with history, review, and rollback.

Externalize config: Store prompts outside code so changes don't require redeploys.

Template variables: Parameterize dynamic parts instead of hardcoding.

Test every change: Run eval suites against prompt edits before release.

Document intent: Comment why each section exists to prevent fearful editing.

Trim ruthlessly: Remove redundant instructions bloating tokens and confusing the model.

Track performance: Tie prompt versions to eval and production metrics.

Build user profiles: Capture preferences, history, and behavior in a retrievable store.

Retrieve context: Inject relevant user signals into each request via RAG.

Balance explore/exploit: Mix safe picks with novel suggestions to avoid a filter bubble.

Combine signals: Blend collaborative filtering with LLM reasoning over context.

Respect freshness: Weight recent behavior and decay stale preferences.

Handle cold start: Fall back to onboarding questions or popularity for new users.

Add diversity: Enforce variety so results aren't dominated by one category.

Measure outcomes: Track engagement/conversion per user, not global averages.

Pre-process images: Deskew, denoise, and enhance scans before extraction.

Route by complexity: Use cheap OCR for clean docs, VLMs only for hard layouts.

Extract to schema: Force structured JSON output with field validation.

Score confidence: Flag low-confidence fields for human review.

Batch process: Group documents to exploit batch pricing and throughput.

Handle failures: Retry, fallback models, and quarantine unparseable docs.

Verify critical fields: Cross-check totals/IDs with rules or lookups.

Monitor accuracy: Sample outputs against ground truth continuously.

Wrap with retries: Exponential backoff and jitter for transient failures.

Add circuit breakers: Stop calling a failing service and fail over fast.

Enforce timeouts: Bound every tool call so one slow API can't stall the agent.

Respect rate limits: Throttle, queue, and back off proactively per provider.

Cache responses: Reuse results for identical or recent calls.

Degrade gracefully: Provide fallbacks or partial answers when a tool is down.

Validate outputs: Schema-check tool responses before trusting them.

Observe everything: Track per-tool latency, error rate, and cost.

Define metrics: Pick clear success measures (resolution rate, CSAT, cost per outcome).

Split traffic: Randomize users into control/variant with proper sample sizing.

Isolate variables: Change one thing (prompt, model, config) per experiment.

Track online + offline: Pair live metrics with offline eval scores.

Ensure significance: Run long enough for statistically valid results.

Guard against harm: Monitor safety/latency/cost so a winning variant isn't secretly worse.

Version experiments: Tie each variant to pinned prompts/models for reproducibility.

Roll out gradually: Ramp winners via canary before full launch.

Filter inputs: Screen prompts for abuse, injection, and disallowed intent up front.

Filter outputs: Run generated text through safety/toxicity classifiers before delivery.

Enforce brand rules: Apply style/tone constraints and banned-term checks.

Use layered defense: Combine model-level, rule-based, and classifier guardrails.

Add human review: Route high-risk or low-confidence outputs for approval.

Set fail-safe defaults: Block or return a safe fallback when guardrails trip.

Red-team regularly: Probe for jailbreaks and edge cases proactively.

Monitor and log: Track violation rates and feed incidents back into rules.

Constrain the schema: Give the model only relevant tables/columns with clear descriptions.

Validate before running: Parse and check generated SQL against a schema and allow-list.

Enforce read-only: Block writes/DDL and run under a least-privilege account.

Guard performance: Add row limits, query timeouts, and reject full-table scans.

Verify results: Show the query and let the user confirm intent before execution.

Use few-shot examples: Ground generation with known-good query patterns.

Handle ambiguity: Ask clarifying questions when the request is underspecified.

Log and learn: Capture failures to improve prompts and examples over time.

Add fallback providers: Route to a backup model/provider when the primary fails.

Retry with backoff: Handle transient errors with exponential backoff and jitter.

Use circuit breakers: Stop hammering a failing provider and fail over quickly.

Abstract the interface: Wrap providers behind a gateway so swapping is seamless.

Degrade gracefully: Serve cached answers or reduced functionality during outages.

Set timeouts: Enforce per-call limits so slow providers don't cascade.

Monitor health: Track provider latency/error rates and alert on anomalies.

Load-shed: Queue or reject low-priority traffic to protect critical paths.

Trim smartly: Drop or compress old turns instead of blindly truncating from the top.

Summarize rolling history: Replace stale turns with running summaries to preserve key facts.

Externalize memory: Store durable facts (preferences, decisions) in a retrievable store, not the prompt.

Retrieve on demand: Pull only relevant past context per turn instead of resending everything.

Separate memory types: Distinguish short-term (working) from long-term (persistent) memory.

Budget tokens: Cap how much history enters each call and prioritize recent + relevant.

Pin critical context: Always keep system rules and task-critical facts in-window.

Expire stale data: Age out or refresh memory so outdated info doesn't mislead.