Building agentic AI systems with modular orchestration

Getting started with agentic AI systems

I call an agentic AI system one that perceives, plans, and acts across more than one tool or API with minimal human intervention. The LLM usually sits in the planning layer, but it is only one part of the setup.

The pieces I plan for are:

• Perception and input handling: parsers, intent detection, and retrieval layers. I keep raw user input, embeddings, and semantic matches separate.
• Planner: the part that turns a goal into steps or sub-tasks. This is usually where the LLM sits.
• Executor or tool invoker: the bit that runs actions, calls APIs, runs scripts, or talks to browsing and databases.
• Memory and state: short-term working memory for the current task, plus longer-term storage for facts and history. Vector indexes are the usual fit for retrieval-backed steps.
• Orchestrator: the coordinator that assigns work, handles retries, and applies safety checks.
• Observability and audit: logs, traces, and semantic traces so I can replay decisions and see why the agent acted.
• Behavioural safety: runtime limits, policy checks, and fallbacks that stop harmful or expensive actions.

Modular orchestration matters because a monolith makes it harder to see what went wrong. Split planning, execution, and tooling into separate modules and each one gets a clear contract: inputs, outputs, and failure modes. That makes testing less painful. It also makes change easier. Swap the LLM, replace the message broker, leave the rest alone.

A simple booking assistant is the clearest example I have:

• The input handler pulls out dates, destination, and constraints.
• The planner lays out the steps: search flights, compare prices, reserve, confirm.
• The executor calls the flight API, payment API, and calendar API.
• The memory layer stores the booking reference and retry state.
• The audit layer records each decision and API response.

That split keeps the payment step behind a stricter safety gate. I would not trust it any other way.

The trade-offs are straightforward:

• Latency versus correctness: more verification means slower responses. I would keep a fast path for low-risk tasks and a strict path for high-risk ones.
• Cost: LLM calls add up. Cache routine decisions and use cheaper models for basic classification.
• Complexity: more modules means more moving parts. Automation for deployment and monitoring matters early.

Implementing modular orchestration in AI

I break implementation into discrete steps and check each one before moving on.

1. Define the capability surface

   • List the concrete tasks the agent must do.
   • For each task, write down the required inputs, expected outputs, and failure modes.
   • Verification: write acceptance tests for the happy path and at least two failures.

2. Design the module contracts

   • For each component, such as planner, executor, and memory, define the function signatures, timeouts, and retry rules.
   • Add a small JSON schema for each message on the bus.
   • Verification: mock each module and run end-to-end tests that check schema conformance.

3. Pick an orchestration fabric

   • Options are a simple HTTP control plane, a message bus such as RabbitMQ or Kafka, or a workflow engine.
   • I favour a lightweight message bus with a controller that manages orchestration state. That keeps components replaceable.
   • Verification: run a load test with concurrent agents and measure queue depth, latency, and error rates.

4. Implement behavioural safety gates

   • Static checks: policy filters that inspect planned actions before execution.
   • Runtime checks: monitors that pause or abort on suspicious patterns.
   • Human approval for high-risk actions: require manual approval for financial or destructive tasks.
   • Verification: red-team the planner with adversarial prompts. Check that the safety gate blocks the harmful plan and logs the event.

5. Add durable memory and retrieval

   • Use a vector store for semantic memory and a relational store for transaction history.
   • Add TTL and pruning for memory hygiene.
   • Verification: seed memory with known facts, query it, and check the retrieved facts match the expected similarity thresholds.

6. Build observability and audit

   • Log decisions, prompts, model responses, and tool outputs. Keep the original prompts for replay.
   • Add measures for successful tasks, retries, aborts, wall time, and cost per task.
   • Add distributed tracing across modules so a request can be followed from input to final action.
   • Verification: replay a recorded session in staging. Check the logs rebuild the decision path and the measures still match.

7. Deploy with progressive rollout

   • Use feature flags or a canary channel. Start with a narrow user set or low-risk tasks.
   • Monitor cost, error rates, and safety gate triggers.
   • Verification: only widen rollout when the automated checks are green. Roll back on thresholds you define.

The bits I keep coming back to are simple. Keep prompts short and treat prompt changes like code. Put credentials and privileged APIs behind a separate executor service. Keep modules stateless where you can and leave state in the memory layer. Test the planner for plan structure, not just the final answer. Track where each decision came from, because when it breaks you need to know which model, prompt, and memory entry drove it.

Case studies and examples

• Internal assistant: I built an assistant that composes emails and schedules meetings. The planner produced a step list. The executor had a strict safety gate before sending any outbound mail. That gate checked recipients against a deny-list and asked for confirmation on external addresses.
• Data enrichment pipeline: an agent enriched records by calling several APIs. I used a message bus and worker pool for parallelism. Observability showed which worker failed most often and exposed flaky API responses. That led to retry rules per API and a caching layer to reduce cost.

Final technical checks before production

• Failure injection: break downstream APIs and check the agent fails safely.
• Cost simulation: run the expected load through pricing models for LLM calls and infrastructure.
• Compliance review: check the audit logs meet retention and access rules.

Build agentic AI systems as separate parts, not one lump. Treat the planner as one module, add safety gates and observability early, and keep the contracts clear. That is the difference between something you can operate and something you only understand when it is already on fire.