Start agent architecture from workflow risk

The main mistake in enterprise agent projects is selecting a framework before defining risk, control points, and failure impact. A good agent architecture starts with workflow analysis:

• Which decisions are reversible vs irreversible?
• Which steps require deterministic guarantees?
• Which outputs need citations or human approval?

These answers determine whether you need a simple chain, a stateful graph, or a multi-agent system.

Four proven agent architecture patterns

1) Router-worker

Use when intent classes are clear and tools are specialized.

• Router selects the right specialist agent
• Worker executes with focused context and tools
• Final synthesizer returns a unified answer

This pattern is highly effective for support or operations use cases.

2) Planner-executor

Use when tasks require explicit decomposition.

• Planner generates a step plan
• Executor agents perform each step
• Verifier checks plan completion and evidence

Great for complex research or multi-step analysis workflows.

3) Stateful graph orchestration

Use when conversation state and branching matter.

• Node-level transitions model business logic
• Explicit state machine prevents hidden behavior
• Checkpointing supports resumability and auditability

The RAG Equity Research Agent uses graph orchestration to coordinate retrieval, market data, and synthesis reliably.

4) Deterministic core + agentic edge

Use for high-compliance environments.

• Deterministic services own policy-critical operations
• Agent layer handles language understanding and user interaction
• Guardrails enforce escalation on low confidence

In DAISI, this balance keeps user experience conversational while preserving enterprise controls.

State and memory boundaries

Enterprise reliability depends on strict state design:

• Keep short-term conversation state separate from long-term memory
• Store tool outputs as typed records, not free-form strings
• Add TTL and deletion policies for compliance-sensitive data

If state boundaries are unclear, debugging and governance both degrade.

Tool contracts and error discipline

Treat tools like APIs with strict contracts:

• versioned schema for every tool call
• explicit timeout and retry policies
• structured error classes (recoverable vs terminal)
• fallback behavior documented per tool

This avoids the common anti-pattern where the agent “hallucinates through” tool failures.

Evaluation strategy for agent systems

Evaluate each layer independently before end-to-end tests:

• Intent routing accuracy
• Tool selection precision
• Task completion success rate
• Human escalation correctness
• End-to-end business KPI impact

Without layer-level evaluation, you can see output failures but not their root cause.

A practical decision checklist

Choose your architecture with these rules:

• Start with the simplest pattern that satisfies risk constraints
• Add specialized agents only when one agent cannot stay reliable
• Keep critical decisions deterministic and auditable
• Instrument every transition with trace IDs

Agent systems scale when architecture choices are tied to operational reality, not novelty.