TBW / AGENTIC AI / ARCHITECTURE
Agent Design and Orchestration
Design AI workflows with explicit state, memory, tools, control loops, failure handling and human checkpoints.
Architecture Before More Agents
Orchestration determines which component owns each decision and how a run progresses. Begin with the simplest pattern that satisfies the task, then add specialist roles only when separation improves control or quality.
Architecture Patterns
Single agent
One bounded role suffices where tools and success conditions are narrow.
Orchestrator + specialists
Separate distinct tasks into workers; an orchestrator routes, validates outputs and owns completion.
Pipeline
Use a stable sequence when each stage has a known input and output contract.
Human-in-the-loop
Pause for review or authorization where an output or action has consequences.
Unnecessary agents multiply handoffs, complexity, latency, cost and failure modes. Compare the extra boundary against a simpler workflow before choosing a multi-agent design.
Orchestration Components
Specify triggers and schedules, event deduplication, routing rules, task state, optional memory, data pipelines and tool contracts. Place policy checks before tool execution and monitoring around every transition. A run should retain its correlation ID and the version of its workflow definition so operators can reconstruct decisions.
Task State Versus Persistent Memory
Task state
Short-lived context needed to complete the current run: status, intermediate outputs, checkpoint and pending approval. Set expiry and clean up after completion.
Persistent memory
Longer-lived information that influences future runs. Require a clear owner, purpose, retention period, access policy and deletion route.
Test memory for stale conclusions, poisoned entries and sensitive information. Do not carry untrusted tool text into authoritative instructions; validate source and scope before reuse.
A Bounded Control Loop
Plan → Act → Observe → Verify → Continue/Stop. Validate each observation against the expected result, not merely tool success. Set step, time and cost budgets; detect repeated cycles and specify completion criteria. Exhaustion or uncertain state should end in a safe stop with an operator-visible reason.
Inter-Agent Communication and Trust
Authenticate sender identity, validate message schemas and authorize each handoff across trust boundaries. Pass only necessary context; a specialist’s output is data to validate, not an instruction with inherited authority. Detect cascading errors by checking dependencies before downstream execution.
Failure and Recovery Design
Classify retryable failures separately from policy denials. Retry bounded transient errors with backoff; choose an alternate tool or fallback only when equivalent permissions and semantics are verified. Escalate ambiguous changes for human review and stop safely when state cannot be reconciled. Document idempotency and rollback for external writes.
Testing the Orchestration
Build scenario suites and recorded-run replay with edge cases, adversarial inputs and prompt injection. Simulate tool outages, timeouts, corrupted responses and stale checkpoints. Verify that policy denials do not turn into alternate-path privilege escalation and that resumed runs do not duplicate actions.
Implementation and Deliverables
Map responsibilities and transitions, prototype a bounded run, test failures and approvals, then release with checkpoint visibility and operator ownership. Deliver an orchestration architecture, agent responsibility map, state design, memory policy, failure model, evaluation scenarios and runbook.
Related: Agentic AI · AI Agents · Identity and Guardrails
Frequently Asked Questions
When does a multi-agent design make sense?
Use it when distinct tasks require separate roles or tools and the added handoffs can be validated; otherwise prefer a single bounded agent or pipeline.
Is task state the same as persistent memory?
No. Task state serves one run; persistent memory can influence later runs and needs independent ownership, retention and deletion rules.
How do you prevent an endless control loop?
Set step, elapsed-time and cost budgets, detect repeated actions, and require explicit completion or safe-stop conditions.
Can one agent trust another agent’s output?
Treat the output as untrusted data. Authenticate its origin, validate its schema and recheck permissions at each boundary.
What happens after a tool timeout?
Determine whether the action may have succeeded before retrying; use idempotency or reconciliation, then escalate if state remains uncertain.
Design a Controlled Run
Share a workflow with its handoffs and failure consequences. We can select an architecture and define checkpoints before implementation.