These insights draw from building AgenticSeek and my current work at CNRS. A core principle: protect your time first, then your system. Agent failures at step 54 after hours of execution are expensive—design for recovery from day one.

1. Build a resilient provider layer first. LLM providers fail for myriad reasons: rate limits, server errors, authentication expiry, transient network issues. Your provider must handle retries with exponential backoff, graceful degradation between models, and comprehensive logging for traceability. Without this foundation, debugging becomes nearly impossible.
2. Cache and mock everything. Don't wait for long-running agent executions to test changes. Implement response caching and mock interfaces so you can replay specific steps deterministically. This investment pays for itself within days of active development.
3. Design for failure, not success. Complex agent systems will fail. Assume it. Add monitoring hooks, self-checks, watchdog timers, and recovery flows. Consider agent-supervising-agent patterns where one agent monitors another's progress and intervenes on stagnation.
4. Evaluate MCP integrations carefully. Many Model Context Protocol implementations in open-source ecosystems are immature. Often, a focused custom control loop tailored to your specific domain outperforms generic integrations in both reliability and latency.
5. Minimize context aggressively. Use compression techniques, split work across multiple specialized agents, and strip unnecessary tokens (including verbose internal reasoning traces). Larger context windows often degrade practical performance due to attention dilution.
6. Maintain goal visibility. Agents drift. Periodically remind the system of its objective, or implement a supervisor agent that can detect deviation and issue corrective prompts. Explicit goal-state tracking prevents many failure modes.
7. Implement circuit breakers for recursion. Agents are prone to subtle infinite loops—repeatedly reformulating the same question, cycling between states, or expanding without convergence. Add hard iteration limits and pattern detection (e.g., semantic similarity between consecutive outputs) with automatic escalation.
8. Prefer fewer, richer tools. Each additional tool increases cognitive load and decision latency. Design tools that return comprehensive, well-structured outputs rather than many single-purpose utilities. Clear documentation in tool descriptions is essential.
9. Cap tool outputs and encourage incremental use. Tools should never return unbounded payloads. Implement output limits (e.g., ≤8k tokens) and design interfaces that encourage stepwise reasoning rather than monolithic operations.
10. Prototype with the best model, then optimize. Start with the strongest available model to validate behavior and establish a performance baseline. Once stable, systematically test if smaller or local models can achieve equivalent results for cost-effective deployment.
11. Choose models for instruction adherence. Not all models handle structured outputs and tool use reliably. From extensive testing, certain model families consistently struggle with format adherence, leading to planner failures and production issues. Validate format compliance rigorously before deployment.
12. Use local models for bulk testing. Reduce iteration costs by running comprehensive test suites on local or low-cost models. Reserve expensive API calls for final validation and edge cases.
13. Keep humans in the loop for high-stakes decisions. AGI is not here. Design review checkpoints where humans validate agent decisions with significant consequences. Build clear escalation paths for uncertain scenarios.
14. Implement comprehensive provenance tracking. Record not just decisions but the complete context: prompt versions, model identifiers, temperature settings, tool versions, and timestamps. Reproducibility and auditability are essential for scientific and production applications.

The recurring theme: invest in tooling upfront. Mocks, caches, recovery mechanisms, and monitoring may seem like overhead early in a project, but they compound in value as complexity grows. Agent systems are inherently difficult to debug—design for observability from the start.