Modern AI systems are no longer a single monolithic model. They are orchestrations of tools, agents, critics, and feedback loops. Khan’s catalog of 17 agentic patterns gives builders the shared vocabulary to assemble these pieces with intention. Here’s how to use that lens to ship resilient, adaptive systems.
Patterns force clarity. Instead of improvising bespoke agent stacks each time, you can reference proven coordination templates: ensembles for resilience, Tree of Thoughts for exploration, reflexive critics for self-checks, or orchestration layers for adaptivity. Shared language accelerates design reviews, debugging, and stakeholder buy-in.
Treat the 17 patterns as LEGO bricks. Mix, extend, and remix them depending on complexity, risk tolerance, and compute budgets. The goal is architectural discipline, not dogma.
Khan outlines 17 motifs. Below are seven foundational ones you’ll reach for first—complete with intuition, strengths, and risks.
| Pattern | Core Idea | Strengths | Risks |
|---|---|---|---|
| Multi-Agent System | Specialized agents collaborate or hand off tasks via shared tools. | Modularity, parallel work, reusable components. | Coordination overhead, complex messaging contracts. |
| Ensemble Decision | Multiple agents propose answers; a voter aggregates the best. | Robustness, diversity, natural fit for A/B comparisons. | Requires trustworthy scoring logic, higher latency. |
| Tree of Thoughts | Branch reasoning into multiple paths, evaluate, then prune. | Deeper exploration, creative leaps, less greedy search. | Token explosion without heuristics; needs guardrails. |
| ReAct Loop | Interleave reflection (“think”) and tool calls (“act”). | Great for sequential decisions and live tool use. | May loop without progress; requires intervention triggers. |
| Meta-Control | An orchestrator agent assigns subgoals or swaps policies. | Dynamic adaptability, clear separation of concerns. | Controller becomes single point of failure; hard to tune. |
| Reflexive Self-Assessment | Agents estimate uncertainty, flag gaps, or retry proactively. | Safer outputs, audit trail for regulators. | Can spiral into analysis paralysis without thresholds. |
| Critic / Reviewer | Dedicated reviewer agents evaluate, red-team, or rewrite. | Catch regressions, codify organizational quality bars. | Requires termination rules; critics can disagree endlessly. |
Simple tasks? Start with a single agent or lightweight ensemble. Multi-stage workflows like underwriting or pharma diligence demand orchestrators and selective Tree-of-Thought exploration.
Set hard caps on branches, retries, and critic passes. Build dashboards that show tokens spent per stage so you can prune aggressively without losing coverage.
Healthcare, finance, or safety-critical domains benefit from reflexive loops, dual reviewers, or failover ensembles. Record confidence scores and rationales for every hop.
Define JSON contracts or typed events for every hand-off. The clearer the schema, the easier it is to swap agents, throttle retries, or stage migrations.
Multi-agent systems fail silently. Ship dashboards that show agent hop counts, critic pass rates, and average token spend per loop.
Critic chains and reflexive reviewers can oscillate forever. Add max iteration caps, decaying confidence scores, or human-in-the-loop interrupts.
Deliberately sabotage an agent to see whether ensembles, critics, and orchestrators recover. Reliability is a team sport.
Shared Vocabulary: Teams can say “ensemble + critic + orchestrator” and instantly picture the flow. Less ambiguity, faster design decisions.
Reusability: Once you build a killer summarizer or evaluator, you can redeploy it anywhere the pattern reappears.
Scaling: As agent counts grow, pattern awareness helps you anticipate load, failure modes, and governance needs.
Education: New teammates learn your system faster when it maps to recognizable archetypes.
Richer pattern stacks often blur causality. Expect more work on traceability standards and agent-level provenance.
How do we certify a system that lets autonomous agents self-repair? Formal methods and constraint-checking critics are early answers.
Meta-controllers that choose the right architecture for each request remain mostly manual. Expect meta-learning breakthroughs here.
Large agent networks can manifest unexpected politics. Simulations and sandboxed rehearsals are becoming mandatory.