Agentic AI Systems

How do you turn a capable model into an agent system you can trust?

Problem: Fast agent output can hide weak intent, broad authority, lost state, and missing proof.

Question: Which five surfaces must be engineered before an agent earns more autonomy?

Decision: Build the system around the model before expanding the model, toolset, or agent team.

This method produces a bounded agent-system contract and an evidence pack.

Inputs

• One outcome the agent must produce.
• The user or operator who owns that outcome.
• The available data, tools, time, cost, and authority.
• A verifier that can contradict the worker.
• A durable state and proof surface.

The Tight Five

Surface	Question	Required output
Intent and specification	What must become true?	Outcome, constraints, acceptance checks, and escalation points.
Context and state	What must the agent know now and remember later?	Bounded context view, source policy, state schema, and retention rule.
Tools and authority	What may the agent change?	Typed tool contracts, permission tiers, side-effect labels, and approval gates.
Harness and control loop	What keeps execution on course?	Trigger, workflow, budgets, checkpoints, recovery path, and stop condition.
Evidence and evolution	How will the system prove and improve itself?	Traces, tests, metrics, failure classes, correction, and next question.

The five form one loop. Intent selects the work. Context makes it legible. Tools make action possible. The harness controls action. Evidence changes the next intent.

Method

1. Frame the outcome.
   • Output: one sentence naming who can do what, under which constraints, with what observable result.
   • Check: a reviewer can judge completion without reading private reasoning.
2. Compose the smallest useful context.
   • Output: a manifest separating task state, durable knowledge, retrieved evidence, and disposable tool output.
   • Check: each item has a retrieval reason. Remove stale facts and duplicate instructions.
3. Bound every action.
   • Output: each tool has a verb-led name, typed input, return shape, auth owner, side effects, idempotency rule, and failure response.
   • Check: irreversible or external actions require explicit authority. Validate tool output before use.
4. Build the control loop.
   • Output: a trigger, workflow, verifier, budget, checkpoint cadence, recovery path, escalation rule, and exact stop condition.
   • Check: restart with no chat history. The system must resume from durable state or fail closed.
5. Prove and ratchet.
   • Output: tool tests, workflow tests, golden trajectories, behavior constraints, trace fields, cost bounds, and a correction rule.
   • Check: independent evidence can replay success, classify failure, and tell the next run what changed.

Choose the Smallest Control Pattern

Need	Start with	Add complexity only when
One known sequence	Prompt chain or state machine	Observations can change the next step.
Unknown next action	Bounded ReAct loop	Dependencies must persist across a long task.
Stable long task	Plan-and-execute graph	Separate roles improve proof more than coordination costs.
Irreversible action	Human approval gate	Evidence supports narrower autonomous authority.
Independent quality check	Maker/checker pair	Diverse specialists add measurable coverage.

Multi-agent systems are not the default. Split work when context isolation, authority boundaries, or independent verification repay the handoff cost.

Problem-Driven Diagnostic

Symptom	Inspect first	Likely correction
Agent ignores the goal	Intent and context	Sharpen the outcome; remove competing instructions.
Agent calls the wrong tool	Tool contract	Improve names, schemas, visibility, and routing evidence.
Agent repeats failed work	Harness state	Persist attempts; add a progress check and retry cap.
Scheduled job works only in chat	Context and state	Make input self-contained; restore from durable state.
Human review becomes a rubber stamp	Evidence and authority	Surface risk, variance, and the required decision.
Cost rises without value	Context, model, and budget	Shrink context, route models, cap calls, or kill the loop.
Multi-agent results conflict	Handoff contract	Name one owner, shared state, merge rule, and escalation path.

Claim Durability

• Law — a force that survives vendor churn. Context is finite. Side effects need authority. Stochastic work needs deterministic evidence.
• Heuristic — a current rule derived from a law. State the force beneath it so the rule can evolve.
• Snapshot — a dated model, framework, price, limit, benchmark, or feature. Keep it replaceable and never load-bearing.

Failure Modes

• Prompt-only control — policy exists as prose but no runtime gate enforces it.
• Context accumulation — every result stays loaded until history buries signal.
• Authority fog — the worker guesses whether it may edit, publish, spend, or message.
• State amnesia — a new session restarts work or repeats a failed action.
• Loop without recovery — retries exist, but checkpoints, rollback, and escalation do not.
• Evidence theater — one polished answer substitutes for trajectory, behavior, cost, and failure tests.
• Coordination tax — specialists exchange more context than they remove.
• Snapshot doctrine — current vendor features become permanent engineering law.

Proof Of Done

The system is ready for its declared authority when:

• the outcome and constraints are explicit;
• the context view is bounded and source-grounded;
• every action has a typed contract and authority tier;
• state survives a fresh session;
• the loop has budgets, checkpoints, recovery, escalation, and a stop rule;
• independent tests and traces prove success and explain failure;
• failed proof demotes the capability from REALITY.

Changes my mind: A simpler system repeatedly meets the same outcome, safety, recovery, and evidence standard at lower coordination cost.

Retrieval

Pull this method before selecting an agent framework, adding tools or memory, designing a long-running workflow, or raising an agent's authority.

Version delta: Reframed a source-specific survey as a five-part practitioner method with diagnostic, recovery, and evidence paths.

Context

• depends-on Delegation — define authority and refusal rights.
• applies-to Agentic Prompt Loops — turn the harness into a runnable loop.
• pairs-with AI Harness — structure bounded execution environments.
• applies-to The AI Engineer's Stack — place the method inside production engineering.
• example Hermes — see the method under scheduled operation.

Sources

• Agentic Engineering — component, pattern, readiness, and long-horizon structure.
• Agentic Engineering repository — curriculum, rubric, evidence rules, and problem-driven index.
• The Agentic Engineering Guide — context, security, operations, orchestration, and production progression.
• Agentic Software Engineering — intent, permissions, escalation, recovery, and deterministic evidence.
• The Hitchhiker's Guide to Agentic AI — architecture, memory, tools, evaluation, and deployment.

Questions

Next question: Which missing proof keeps this system from earning its next authority level?