Task decomposition
Model goals, steps, state, dependencies, stop conditions, and points where judgment is required.
AI Agents
Design agentic systems that can reason across context, use approved tools, and hand control back to people at the right moments.
01 / THE PROBLEM
Autonomy should be earned task by task.
An agent that can act without a clear mandate, permission model, stop condition, or review path creates operational uncertainty instead of leverage.
A strong fit when
- Multi-step work that crosses systems or knowledge sources
- Teams exploring supervised agent workflows
- Products that need goal-directed assistance beyond a single prompt
02 / THE SYSTEM
Capabilities connected around the outcome.
01 02 Tool and permission design
Expose only the actions and data the agent needs, with traceable authorization boundaries.
03 Human checkpoints
Design approvals, escalation, correction, and resumption as first-class workflow states.
04 Agent evaluation
Test task completion, tool choice, policy adherence, recovery, cost, and latency.
03 / APPROACH
Make the risky decisions testable early.
We decompose the workflow into observable tasks, define tool and data permissions, set checkpoints, and evaluate behavior before expanding autonomy.
Typical outputs
- Agent opportunity map
- Workflow and state model
- Tool and permission architecture
- Evaluation scenarios
- Supervised production implementation
Working principles
- Bound the job
- Least-privilege tools
- Trace every consequential action
- Expand autonomy with evidence
04 / Practical answers
Before we start.
The answer depends on the data, providers, hosting model, user permissions, retention requirements, and consequence of the use case. An AI engagement should document approved sources, data flows, model and tool access, retention choices, permission boundaries, human review, logging, and deletion responsibilities. No sensitive data should be sent to a model or service merely because it is technically available.
Related thinking
An AI agent needs a permission model before a prompt
A practical approach to defining tools, data boundaries, approvals, and recovery before an agent enters a real workflow.
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A practical evaluation plan for agent tasks, tools, policies, recovery, cost, latency, and human review.
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