agentic-ai-developer

Agentic AI Developer

When to Use

• Implementing agent loops with tools: plan → act → observe → stop
• Designing tool/MCP schemas, auth, timeouts, and sandbox boundaries
• Building multi-agent workflows with routing, handoffs, and fan-out/fan-in
• Persisting agent state, checkpoints, thread memory, and resume semantics
• Adding human-in-the-loop approval, edit, or reject gates on risky tool calls
• Hardening agents: retries, idempotency keys, cancellation, and budget caps
• Instrumenting traces, spans, and trajectory logs for debugging and eval
• Running trajectory evals, golden sets, and regression gates before release
• Shipping agentic apps via API, queue workers, or durable workflow engines

When NOT to Use

• Training or fine-tuning foundation models, classical ML pipelines → ai-engineer, ai-researcher
• AI ops cadence, vendor contracts, rollout governance without implementation → ai-lead-ops
• Internal developer platform, golden paths, Backstage—no agent runtime → platform-engineer
• Generic service/API work with no agent loop, tools, or orchestration → senior-software-engineer
• Adversarial red-team campaigns and jailbreak harnesses only → ai-redteam
• Corporate AI policy, risk tiering, model cards without build → ai-risk-governance
• Pre-flight architecture or production-readiness review without building → build-validator
• Multi-agent system topology, routing, protocols, and fleet-level failure at architecture/engineering depth → multi-agent-system-engineer
• High-level multi-agent whiteboard without implementation → agent-designer (external skill; use conceptually)

Related skills

Need	Skill
Broader LLM apps, RAG, model routing, cost/latency	ai-engineer
AI production ops, incidents, release gates	ai-lead-ops
Platform golden paths, IDP, developer portals	platform-engineer
Service design, APIs, code quality without agent focus	senior-software-engineer
Prompt injection, tool abuse, safety eval campaigns	ai-redteam
Governance, risk tiers, policy mapping	ai-risk-governance
Go/no-go plan or architecture validation	build-validator
Persistent memory stores and retrieval design	ai-memory-developer
Context packing and token budgeting	ai-context-engineer
Prompt templates and judge rubrics	prompt-engineer
Multi-agent system topology, routing, DAG, fleet observability	multi-agent-system-engineer
Multi-agent whiteboard without code (conceptual)	agent-designer (external)

Core Workflows

1. Shape the agent runtime

1. Define the user job, success metric, and stop conditions
2. Choose runtime shape: single loop, supervisor + workers, or graph/DAG
3. List tools/MCP servers; classify read vs write vs irreversible
4. Set budgets: max steps, tokens, wall time, cost per session
5. Decide checkpoint/resume and tenancy (thread_id, org_id)

See references/agentic_ai_developer_scope.md for scope boundaries and deliverables.

2. Implement loop + tools

receive task → plan (optional) → select tool → execute → observe → repeat | finalize

Checklist:

• Tool schemas are narrow; descriptions say when not to call
• Timeouts, retries, and idempotency on side effects
• Errors surfaced once to the model; no infinite retry loops
• Secrets never returned in tool results or traces

See references/agent_loop_tools_and_mcp.md for MCP and schema patterns.

3. Orchestrate multiple agents

• Assign roles: planner, executor, critic, specialist
• Handoff payload: goal, constraints, artifacts, open questions
• Avoid duplicate tool access unless idempotent; centralize dangerous tools
• Use fan-out/fan-in for parallel research; merge with structured reducer

See references/multi_agent_orchestration_and_handoffs.md for routing and handoff contracts. For system-level topology, fan-in policy, and cross-agent failure matrices, use multi-agent-system-engineer.

4. State, memory, and HITL

• Separate ephemeral scratchpad vs durable thread state vs long-term memory
• Checkpoint after each tool batch or subgraph node for resume
• HITL on tier-2+ actions: approve, edit args, or reject with reason
• Time out stalled human approvals; default-deny on expiry

See references/state_memory_and_hitl.md for checkpoint and approval patterns.

5. Reliability, observability, and evaluation

• Trace: session_id, span per model/tool step, redacted inputs/outputs
• Metrics: success rate, steps to completion, tool error rate, p95 latency, cost
• Eval: golden trajectories, tool-call correctness, task success (human or judge)
• Gate releases on regression suite; canary new prompts/graph versions

See references/reliability_observability_and_evaluation.md for eval and SLO patterns.

6. Security and production deployment

• Sandboxed tool execution; least-privilege credentials per tool
• Treat tool results and retrieved docs as untrusted input (injection aware)
• Deploy: sync API for short tasks; queue or durable workflow for long runs
• Kill switch, feature flags, and versioned prompts/graph definitions

See references/security_and_production_deployment.md for deployment topologies.

When to load references

Topic	Reference
Role scope, deliverables, boundaries	references/agentic_ai_developer_scope.md
Agent loop, tools, MCP	references/agent_loop_tools_and_mcp.md
Multi-agent routing and handoffs	references/multi_agent_orchestration_and_handoffs.md
State, memory, checkpoints, HITL	references/state_memory_and_hitl.md
Retries, tracing, trajectory eval	references/reliability_observability_and_evaluation.md
Sandboxing, injection, deployment	references/security_and_production_deployment.md

Framework pointers (optional)

Use framework docs for API specifics; this skill stays pattern-first:

Pattern	Typical home
Stateful graph, interrupts, checkpointing	LangGraph-style graphs
Subagents, filesystem memory, HITL middleware	Deep Agents-style harness
Programmatic cloud/local agents, MCP in CI	Cursor SDK-style agents

Do not duplicate full framework tutorials—implement the contracts above in the stack the team chose.