Over the last several weeks, we’ve discussed AI agents and the crucial inclusion of orchestration layers to improve their performance. Business and analysts alike are coming to the conclusion that AI agents are unlikely to dethrone entrenched software services, but, rather, will interact with them in much the way humans do now. As the software business analysis team from Deutsche Bank recently noted, AI model providers are, “positioning themselves and their agents to be an orchestration layer on top of existing and incumbent systems,” and that it, “remains very difficult to replicate or displace much of the knowledge, metadata, and workflows incumbent systems have amassed.” Hence the need for tools that are capable of managing that data and associated tasks efficiently.
This week, we cook up a recipe to build both agents and the orchestration layer. We start small and build something that behaves well under load conditions, then tighten it up so it behaves well under messy conditions, too.
Step 1: Pick one job the agent will do
Choose a single responsibility that has a clear outcome. Good first jobs include answering questions from a known knowledge base, drafting a first pass for a support ticket reply, summarizing a meeting transcript into action items or preparing a short research brief.
Avoid instructions such as “manage projects” or “handle anything the user asks.” Those are hard to put boundaries around and hard to test.
Write one sentence that will define success. For example, “Produce a customer reply that cites the right policy and includes next steps.” That sentence becomes the basis for every decision that comes next.
Step 2: List what the agent is allowed to interact with
Make a short inventory of inputs and actions.
Inputs include things like a ticket, a chat thread, a document set or a product catalog. Actions include things like searching, reading a document, creating a draft, opening a case or scheduling a follow up. Keep this list specific. If it’s not on the list, the agent cannot do it.
This is also where you decide what the agent must never do, such as sending messages directly, issuing refunds, changing records or accessing sensitive data.
Step 3: Choose the tools, then lock them down
Give the agent only the tools it needs for the job. Tools can be internal APIs, search, database queries, calculators or structured actions such as “create draft response.”
Define tool inputs and outputs in a structured way. The more structured the tool interface, the easier it becomes to validate and debug.
Add guardrails at the tool level. If a tool can change the state of something, require approval or restrict it to a safe mode. If a tool can retrieve sensitive data, filter what can be returned.
Step 4: Design the agent loop in plain language
An agent is a loop so it should be treated like one.
Decide how the agent will move through these phases:
The agent reads the task. The agent forms a plan. The agent takes one action. The agent checks what happened. The agent repeats until it reaches an ending condition that will stop the loop.
Keep the plan lightweight and specific. You want enough structure to guide tool use, not a long essay.
Step 5: Define memory and context rules
Decide what the agent should remember within a session and what it should not.
A simple rule set works well. Remember the goal, key facts and what tools have already been used. Do not keep full transcripts unless they are needed. Do not keep private data beyond what is required for the task.
If the agent works across multiple sessions, separate short-term context from longer term notes. Longer term notes should be curated, not automatically generated as a narrative because the agent can get lost in superfluous detail.
Step 6: Add stopping conditions before you add autonomy
Most agent failures happen because the agent does not know when to stop.
Define at least three types of stopping conditions for the agent:
- A completion condition that describes what done looks like
- A limit condition based on steps, time, or spend
- An escalation condition that triggers when the agent lacks information, confidence or permission
If you do nothing else, do this step. It prevents the common pattern of endless loops and inefficient wandering.
Step 7: Build a simple validation gate
Before anything is presented to a user, add a check.
Validation can be as simple as verifying the output has required fields, cites a source, follows a template, or matches a known schema. It can also include a second pass that tries to spot missing steps or unsupported claims.
This is where you decide what happens on failure. Retry, rerun retrieval, ask the user a clarifying question, or hand off to a human.
Step 8: Put the agent behind an orchestration layer, even if it’s small
At first, the orchestration layer can be minimal, but it should still exist as a distinct piece.
The layer’s job is to manage state, route the task, apply limits and run validation. The agent’s job is to reason and act inside those boundaries.
This separation makes it easier to improve the system later without having to rewrite everything.
Step 9: Add routing logic
Routing can be simple at the start.
Route tasks to the agent only when they fit the job it was designed for. Route everything else to a safer workflow or to a human. If you’re supporting multiple agents, route by task type, risk level or required tools.
A simple router prevents the agent from being used as a universal solution to every request.
Step 10: Add an approval step for high-risk actions
Any action that changes data, sends messages, or triggers external effects really should have an approval step unless the system operates in a fully controlled environment.
Approval can be as simple as a human click, a second agent acting as a reviewer or a policy check that the work product must pass before the action is allowed to continue.
Do not treat approval as a nice-to-have especially if the agent has the ability to change a state: it’s how to keep an agent useful as you minimize risk.
Step 11: Introduce multi agent patterns only when needed
If the job becomes too complex for one agent, split the roles.
A common first split is planner and executor. The planner outlines the approach to the task and the executor uses tools and produces outputs. A second split adds a reviewer that checks results before they move forward.
Your orchestration layer should manage these handoffs, decide the order and preserve the shared context.
Do not add multiple agents just because it sounds advanced. Add them when a single agent loses focus or errors begin to go unnoticed.
Step 12: Log what matters and measure the outcomes
If you cannot see what happened, you cannot improve it.
Log the task, the plan, tool calls, tool outputs, validation results and stopping reason. Track simple metrics such as success rate, escalation rate, average tool calls per task, average run time and spend per task.
This observability tells you whether your orchestration is actually helping or just adding complexity.
Step 13: Add fallbacks and degrade gracefully
Decide what happens when things go wrong.
If retrieval fails, use a default message or request more information. If a tool call fails, retry once then switch paths. If validation fails repeatedly, escalate. If the agent is unsure, stop early.
A system that fails cleanly will earn more trust than a system that fails spectacularly.
Step 14: Tighten the loop through testing and controlled rollout
Test with real examples before broad release.
Start with offline testing using a set of representative tasks. Then run in shadow mode where outputs are generated but not used. Then release to a small group with strong monitoring.
Tighten the agent’s boundaries based on what you observe and expand only when performance is stable.
Agents and orchestration can be a force multiplier for your business when given precise instructions, defined success parameters, visible off-ramps and the ability to fail gracefully. When this recipe is followed, you’ll end up with a consistent dish every time.
