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This guide explains six practical patterns, how they differ, how to combine them, and the production controls that make an agentic system reliable and safe.
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What an agentic system actually is
Azure Databricks defines an agent as an AI system that perceives a request, decides what to do, and acts to achieve a goal. In implementation terms, that is a loop: the model receives instructions and context, chooses a next action, invokes a tool or produces an answer, observes the result, and repeats until it can finish.
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Decide the workload before choosing a pattern
Write down the operating requirements before drawing an architecture. These requirements determine whether you need one agent, several specialists, or a controlled mixture.
- Task structure: Is the workflow fixed and repeatable, or does each request require different decomposition?
- Latency target: Is a response needed interactively, or can work run in the background?
- Quality target: Is an adequate answer sufficient, or must the system pass review, testing, or compliance checks?
- Inference budget: How many model calls and tokens can each request consume?
- Failure tolerance: Can an incorrect step be retried, or could it create an irreversible financial, legal, safety, or data-protection impact?
- Human involvement: Which actions require approval, and at what point should a person see the evidence?
- Ownership and trust: Are all tools and agents operated by your team, or must the system communicate with externally owned agents?
- State requirements: Does the task need only current-turn context, or durable memory across sessions?
Use these answers to set explicit budgets and boundaries. A system that cannot state its maximum runtime, call count, tool permissions, and approval points is not ready for production, regardless of how capable its model appears in a demonstration.
The six architectural patterns
1. Single-agent loop
A single model receives a system prompt, a defined tool set, and the user request. It interprets the goal, plans one or more steps, calls tools, incorporates observations, and synthesizes the result. The harness controls the loop and decides when the agent has returned a final response.
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This is the recommended starting point for most teams. Google Cloud advises refining prompts and tool definitions with a single agent before introducing multi-agent complexity. You can establish tool schemas, permissions, telemetry, retries, and evaluation with one reasoning process, then determine which limitation actually requires decomposition.
When it fits
- The task is moderately open-ended but uses a small, coherent set of tools.
- One model can maintain the necessary context within the available window.
- Latency and cost targets do not require concurrent work.
- A single authority should make the final decision.
Risks and controls
A single agent can become a long, opaque chain of calls. Set a maximum number of iterations, per-tool timeouts, token limits, and an overall deadline. Return structured tool errors so the model can recover or explain a failure instead of hallucinating a successful action. Keep high-impact operations behind an approval gate even when the agent is otherwise autonomous.
2. Sequential pipeline
Specialized agents execute in a fixed order, passing each output to the next. A typical pipeline might extract facts, normalize them, draft an answer, run a policy check, and format the result. Each stage has a narrow instruction set and an explicit input and output schema.
Sequential design is efficient and predictable for repeatable workflows. Because the order is known, you can test each stage independently, cache stable outputs, and identify the stage responsible for a failure. The trade-off is rigidity: if an early result changes the required path, a fixed pipeline may perform unnecessary work or lack a branch for the new condition.
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When it fits
- The business process has stable stages and clear hand-offs.
- Each stage benefits from a different prompt, model, or permission set.
- Auditability and deterministic routing matter more than adaptability.
Design details
Define a typed contract for every hand-off. Include provenance, confidence or validation status where relevant, and an explicit error field. Do not pass an entire conversation to every stage by default; send the minimum context needed for that stage. A failed stage should stop or route to a known recovery path rather than silently supplying an empty or guessed value.
3. Parallel fan-out and synthesis
Independent agents receive the same request or separate subtasks and run concurrently. A synthesizer then combines their outputs into one result. Parallel research, independent code reviews, or multiple compliance checks are common examples because the branches do not depend on one another.
Fan-out reduces elapsed latency when the branches can run at the same time and can provide diverse perspectives. It also increases immediate resource use, token consumption, and coordination work. Every branch adds another possible timeout, malformed output, or contradictory conclusion. The synthesizer needs a defined policy for disagreement and missing results.
When it fits
- Subtasks are genuinely independent and can execute without shared mutable state.
- You need several perspectives or checks rather than one chain of reasoning.
- Wall-clock latency is important enough to justify higher concurrent spend.
Operational safeguards
Set a branch deadline and decide whether synthesis can proceed with partial results. Use a concurrency limit to protect downstream services. Tag every branch output with its role and source so the synthesizer does not treat repeated copies of the same evidence as independent confirmation. If branches can perform writes, isolate them or require approval before any side effect.
4. Iterative loop and refinement
An iterative architecture repeats a sequence until a termination condition is met. One agent may draft while another critiques, or a generator may produce an artifact that a tester evaluates before the system revises it. The loop continues until it reaches a quality threshold, passes required checks, or hits an iteration limit.
This pattern suits drafting, test-and-fix workflows, self-correction, and tasks where the first attempt is unlikely to satisfy all constraints. It is also the easiest pattern to make unbounded. Each iteration adds model calls, tool activity, latency, and opportunities for a bad change to compound.
Required termination design
- Define a measurable stop condition. Examples include a validator returning pass, a score exceeding a stated threshold, or a fixed set of tests succeeding.
- Set hard limits. Cap iterations, tokens, wall-clock time, and total tool cost per request.
- Track progress. Record the score, failed checks, and changed artifacts at each iteration so the loop cannot continue while making no improvement.
- Specify failure behavior. Return the best validated version, escalate to a person, or stop with an explicit reason when the limit is reached.
A loop should never decide that it is finished solely because the model says it is. Independent validators, tests, or policy checks should provide the evidence used by the stop condition.
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5. Hierarchical coordinator-worker
A root agent receives an ambiguous goal, decomposes it into subtasks, delegates those subtasks to specialized workers, and integrates their results. Google Cloud identifies research, planning, and synthesis as suitable examples. Workers can be single agents, sequential mini-pipelines, or parallel branches.
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Hierarchy improves coverage when the work cannot be decomposed in advance. The coordinator can choose specialists based on the request and allocate effort where it is needed. The cost is substantial design, debugging, and maintenance complexity. You must govern delegation, prevent duplicated work, preserve provenance, and ensure that the coordinator does not grant a worker more authority than the original request allows.
Boundaries for a coordinator
- Give the coordinator a registry of available worker capabilities and explicit permission scopes.
- Require a structured task description containing objective, inputs, expected output, deadline, and allowed tools.
- Propagate the user’s authorization and data restrictions to every worker.
- Set a total delegation budget so recursive spawning cannot grow without limit.
- Have the coordinator reconcile contradictory outputs using stated rules or human review, not an unexamined majority vote.
6. Hybrid orchestration
Production systems often combine the other patterns. Microsoft describes a hybrid example with serial template selection and grounded generation, concurrent compliance checks, and human review. A model-directed segment can choose a route for an ambiguous request, while fixed serial or parallel sections provide predictable controls where they matter most.
Hybrid design is useful when one pattern cannot meet every requirement. Keep the boundaries visible: document which transitions are deterministic, which are model-directed, and where state or permissions change. Treat each boundary as an interface with schemas, timeouts, telemetry, and an explicit fallback.
Pattern comparison
| Pattern | Determinism and adaptability | Latency and inference cost | Quality and diversity | Failure isolation | Operational burden |
|---|---|---|---|---|---|
| Single-agent loop | Moderately adaptable; path is model-directed | Usually the lowest orchestration overhead; variable call count | One reasoning perspective | Failures can affect the whole loop | Lowest starting complexity |
| Sequential pipeline | Highly deterministic; least adaptable to unexpected conditions | Wall-clock time accumulates across stages | Specialization improves consistency | Stage-level isolation and diagnosis | Moderate; contracts and routing are required |
| Parallel fan-out | Fixed branches with concurrent execution | Lower elapsed time, higher immediate resource and token use | Multiple perspectives or checks | Branch failures can be isolated if synthesis supports partial results | Higher; concurrency and reconciliation must be managed |
| Iterative refinement | Adaptable through feedback, bounded by explicit limits | Potentially the highest variable cost and latency | Can improve quality over attempts | Validator and iteration boundaries expose failures | High; stopping and regression controls are essential |
| Hierarchical coordinator-worker | Most adaptable for ambiguous decomposition | Variable and often expensive because of delegation and synthesis | Broad specialist coverage | Worker-level isolation is possible but coordination failures remain | Highest design, debugging, and maintenance burden |
| Hybrid orchestration | Balances fixed controls with model-directed routing | Depends on the mix; optimize each segment separately | Can combine specialization, diversity, and review | Boundaries provide targeted containment | High; many interfaces must remain observable |
A practical selection procedure
- Test the non-agentic option. If a deterministic program, retrieval query, or single model call satisfies the requirement, it may be cheaper and easier to govern.
- Prototype a single-agent loop. Limit tools and permissions, instrument every model and tool call, and collect representative tasks.
- Measure the actual bottleneck. Determine whether failures come from missing specialization, excessive latency, insufficient quality, context limits, or an inability to plan the next step.
- Add only the pattern that addresses that bottleneck. Use sequential stages for stable specialization, parallel branches for independent work, iteration for measurable refinement, and hierarchy for ambiguous decomposition.
- Set budgets before scaling complexity. Specify maximum calls, tokens, runtime, concurrency, retries, and tool spend per request.
- Run adversarial and failure tests. Include malformed tool responses, timeouts, contradictory branch outputs, prompt injection, stale memory, revoked credentials, and partial outages.
- Compare against the simpler baseline. Keep the added pattern only if it improves the target metric enough to justify its cost and operational burden.
Production building blocks
Architecture patterns describe control flow; a production system also needs the surrounding platform. Azure Databricks groups the core challenges around model access, durable compute, context, quality measurement, and governance and control.
Agent loop and model
The harness applies system instructions, selects the next action, validates tool arguments, and decides whether to continue. Choose a model and context budget that match the task rather than assuming the largest model is always best. Preserve the raw request, decisions, tool arguments, observations, and final response for later evaluation.
Tools and execution environments
Tools may include APIs, retrieval systems, sandboxes, and MCP servers. Give each tool a narrow schema and return machine-readable success and error states. Sandboxes should isolate untrusted code and data. For write operations, support dry runs, idempotency keys, and explicit confirmation.
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Durable state and memory
Session state keeps the current task coherent across calls. Durable memory stores facts across sessions, but it creates privacy and integrity risks. Define what may be stored, how it is corrected or deleted, which user or tenant can retrieve it, and how stale or conflicting memories are handled. Do not allow a retrieved memory to override current authorization.
Agent server and runtime
An authenticated server must scale workers, recover from crashes, enforce deadlines, and resume durable work without duplicating side effects. Separate orchestration state from transient process memory so a restart does not erase the task or repeat an irreversible action.
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Observability and evaluation
Capture distributed traces for model calls, tool invocations, retrievals, memory access, delegation, retries, and approvals. Evaluate complete trajectories, not only final text. Useful measures include task success, policy violations, tool-error recovery, latency, token and tool cost, escalation rate, and the quality of evidence supporting the result. Use regression suites and expert review for changes to prompts, models, tools, and routing.
Governance and control
Enforce identity, least-privilege permissions, model and data policies, rate limits, guardrails, approval gates, audit logs, and usage accounting. Azure Databricks describes managed memory, durable execution, tracing, evaluation, policy controls, MCP tools, and framework and model choice as capabilities of its Agent Bricks offering; its concepts page was last updated October 13, 2026. Those capabilities illustrate the control categories a platform must provide, whether built in-house or adopted as a managed service.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.MCP and A2A: choosing an interoperability boundary
Use MCP for tools and data
Microsoft recommends MCP for secure, authenticated access to tools and data, including Microsoft 365 service tools. MCP is appropriate when your host agent should retain orchestration and synthesis while connecting to a capability through a standard interface. Apply least-privilege scopes, authenticate the caller, validate typed arguments, and return concise, descriptive errors.
Use A2A for externally owned agents
A2A is better when another agent is externally owned or opaque and must negotiate capabilities and exchange messages across platform boundaries. Treat the remote agent as an independent service: authenticate both sides, define the payload schema and size limits, set deadlines, and make the trust and data-sharing contract explicit. Do not assume that an A2A peer follows your internal prompt, memory, or safety policy.
Do not confuse the two
MCP exposes tools and data to a host that controls the orchestration. A2A connects agents that may each control their own reasoning and runtime. A system can use both: an internal coordinator may call MCP tools while delegating a specialized task to an external A2A agent. In either case, transfer concise context rather than an entire transcript, preserve provenance, and require human approval for high-impact actions.
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Reliability, safety, and operations
AWS’s Agentic AI Lens, published June 10, 2026, emphasizes that one request can trigger multiple model calls, tool invocations, memory retrievals, and inter-agent communications. Every additional step adds latency, cost, and a failure surface. Autonomous action, stochastic behavior, collaboration overhead, and persistent-memory privacy and integrity issues require purpose-built oversight and bounded autonomy.
Budgets and timeouts
Define per-request limits for wall-clock time, model calls, tokens, tool calls, parallel branches, memory reads, and delegation depth. Use shorter child deadlines than the parent deadline so the system has time to synthesize or recover. Expose budget exhaustion as a clear status rather than silently truncating work.
Retries and idempotency
Retry only transient failures and use exponential backoff with a cap. Read operations are usually safer to retry than writes. For a write, attach an idempotency key and make the receiver return the original result when the same key is submitted again. Never let a model decide that an unknown write outcome is safe to repeat.
Approval gates
Insert a human checkpoint before actions that move money, change access, publish externally, delete data, or create legal or safety consequences. Show the proposed action, affected resources, evidence, and expected side effects. Record who approved it and which version of the plan was approved.
Graceful degradation
Design a useful fallback for each dependency. If a parallel branch times out, synthesize the completed branches and identify what is missing. If memory is unavailable, continue only when the task can be completed safely without it. If a validator is down, do not claim success; pause, route to review, or return an explicitly unverified result.
Security and privacy
Propagate user identity and authorization through every agent and tool call. Keep secrets out of prompts and traces, redact sensitive fields where possible, and isolate tenants in state and memory stores. Treat retrieved documents, tool outputs, and remote-agent messages as untrusted input that can contain instructions; the harness, not the model, must enforce permissions.
Common failure modes and fixes
| Symptom | Likely architectural cause | Fix |
|---|---|---|
| Costs rise unpredictably | Unbounded loops, recursive delegation, or excessive fan-out | Set hard budgets, concurrency limits, and delegation depth; emit per-request cost metrics |
| Responses are slow despite capable models | Independent work is serialized or child timeouts exceed the parent deadline | Parallelize only independent branches and use staged deadlines |
| Specialists produce incompatible outputs | Loose hand-off contracts or missing provenance | Use typed schemas, role labels, validation, and a reconciliation policy |
| The agent repeats a failed action | Retries are not idempotent and tool errors are ambiguous | Return structured errors, classify transient failures, and require idempotency keys for writes |
| Quality improves in demos but regresses later | Evaluation checks final wording but not the trajectory | Trace and score tool choices, evidence, policy compliance, and recovery behavior |
| A remote agent receives too much data | Whole transcripts are forwarded without a sharing boundary | Send a minimal typed task payload and enforce field-level data policy |
| Memory causes unsafe or stale decisions | Persistent facts lack ownership, expiry, correction, or authorization checks | Govern retention and deletion, attach provenance and timestamps, and reauthorize every action |
A reference decision matrix
Use this shorthand after measuring the workload:
- One model call or deterministic code: avoid an agent.
- Open-ended request with a small tool set: single-agent loop.
- Stable stages with clear contracts: sequential pipeline.
- Independent checks or perspectives under a latency target: parallel fan-out.
- Draft, test, critique, and revise with objective checks: iterative loop.
- Ambiguous goals requiring dynamic decomposition: hierarchical coordinator-worker.
- Mixed requirements: hybrid orchestration, with fixed controls around model-directed sections.
Reassess the choice when the workload, model, tool permissions, data sensitivity, or service-level objective changes. An architecture is not mature because it has many agents; it is mature when its behavior, cost, authority, and failure handling are measurable and bounded.
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Build the smallest architecture that can meet the requirement, instrument it from the first prototype, and promote complexity only when evidence identifies a specific bottleneck. Single-agent loops provide the baseline. Sequential, parallel, iterative, and hierarchical patterns each solve a different structural problem, while hybrid orchestration combines them under explicit interfaces. MCP is the tool-and-data boundary; A2A is the cross-agent boundary. In production, budgets, typed contracts, least privilege, durable state, trajectory evaluation, approval gates, and graceful degradation matter as much as model selection.
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