Agentic workflows: Designing multi-step systems for reliable AI operations

Agentic workflows are multi-step systems that combine prompt design, tool use, state management, and deterministic routing to solve complex tasks reliably. This article explains why agentic workflows matter for building dependable AI systems, what design principles to apply, a recommended prompt structure and interface contracts, a detailed example workflow you can implement, and a QA-first approach to common failure modes. You’ll leave with implementation-focused practices and checkpoints to move from experiments to repeatable, observable production agents. (docs.langchain.com)

Core principles behind the workflow (Agentic workflows)

Designing multi-step systems requires treating the agent as a deterministic orchestration of smaller, testable components rather than a single black-box call. Agentic workflows rely on four core principles: separation of concerns, explicit state, small composable tools, and observability. Each principle reduces brittle behavior and helps isolate failures during runtime. (docs.langchain.com)

Separation of concerns: split responsibilities into focused subagents or nodes (for example: intent routing, data retrieval, synthesis, and action execution). Focused agents can use specialized prompts, tool sets, or models, which improves correctness on narrow tasks and simplifies testing. This idea is central to multi-agent and graph-based workflow approaches. (blog.langchain.com)

Explicit state and contracts: capture the workflow state (inputs, intermediate artifacts, choices, and metadata) in a structured store or state graph. Define input/output schemas for each node and validate them at runtime (use JSON schemas or zod-style validators). Explicit state prevents silent format drift and makes steps replayable and debuggable. (docs.langchain.com)

Composability with small tools: expose functionality through small, well-documented tools (APIs, MCP servers, functions) with one responsibility each. Tools should validate inputs and return guarded structured outputs. Composable tooling reduces the cognitive load on the model and decreases the surface area for side effects. (openai.github.io)

Observability and evaluation: instrument every agent and step with logs, structured traces, and deterministic checkpoints. Record the LLM prompts, model responses, tool calls, and final outputs so you can run offline evaluations and reproduce failures. Platforms for agent engineering increasingly emphasize observability as a first-class capability. (docs.langchain.com)

Recommended prompt structure

For repeatable agentic behavior, build prompts that are modular, machine-parseable, and explicit about responsibilities. Use a layered format: system instructions, role and capability declaration, step specification with schemas, and guarded stopping conditions. Below is a minimal structure to use as a template.

1. System block (static): concise operational mandate and global guardrails. Example: “You are an agent responsible for extracting product specs from source documents and returning validated JSON conforming to the provided schema.” This block should include safety and trust constraints but avoid operational details that change per task. (docs.langchain.com)

2. Role & capabilities (runtime): list the tools available, what each tool does, and when to call them. Keep each tool description to one short sentence and attach a schema or example for valid inputs/outputs. This helps the model choose correctly and enables input validation. (openai.github.io)

3. Step-by-step directives (structured): enumerate the desired stages (e.g., parse, verify, enrich, commit). For each stage provide the expected output format and a short success criterion. Ask the model to annotate its choices in a structured marker (JSON or YAML block) so the orchestration layer can parse the response deterministically. (arxiv.org)

4. Validation and fallback rules: include explicit tests the model must run before concluding (schema validation, confidence checks, tool-output comparisons) and a deterministic fallback path (ask for clarification, escalate to a human, or execute a safe no-op). These reduce silent hallucinations and undefined behavior. (openai.github.io)

Practical tip: favor structured outputs (JSON with an explicit top-level “status” and “diagnostics” fields) rather than free-text conclusions. Structured outputs let the orchestrator enforce gating rules (for example, only accept outputs where status==”validated”). (docs.langchain.com)

Example workflow (step-by-step)

Below is a concrete, implementable workflow pattern for a document ingestion pipeline that extracts, verifies, enriches, and stores facts for downstream search. The pattern is intentionally generic so it maps to either a linear workflow or a multi-agent graph where each node is an agent with its own prompt and toolset. (docs.langchain.com)

1. Ingest & preprocess: deterministic code pulls raw documents, normalizes encodings, runs OCR or text extraction, and computes metadata (source, timestamp, checksum). Log the raw artifact and checksum to the state store. This step is purely deterministic and should not call the LLM. (docs.langchain.com)

2. Intent classification & routing agent: a lightweight classifier (rule-based or small model) determines whether the document requires extraction, classification, or human review. Route to the appropriate subagent or human queue. Keep routing decisions auditable. (docs.langchain.com)

3. Extraction agent: LLM agent performs structured extraction using a constrained prompt that includes the expected JSON schema and two in-context examples. The agent is allowed to call a “validate” tool (a schema validator). The agent must return both the extracted JSON and a short “trace” (reasoning summary) referencing which sections of source text were used. Store the trace for QA. (arxiv.org)

4. Verification agent: independent agent runs cross-checks — compare extracted facts against a curated knowledge base or via a separate API (tool call). If mismatches occur above a threshold, mark the item as “needs_review” and attach conflicting evidence. This independent verification reduces single-agent confirmation bias. (blog.langchain.com)

5. Enrichment agent: when verification passes, optionally call enrichment tools (entity linking, canonicalization, or external APIs). Enrichment tools must return structured outputs and include provenance references (source URL, API response ID). Limit enrichment to idempotent, side-effect-free calls where possible. (openai.github.io)

6. Commit & audit trail: deterministic commit of validated records to storage with a full audit trail (inputs, prompts, model responses, tool calls, checksums). Emit a final manifest that includes a unique record id and diagnostic fields (latency, model version, confidence metrics). This manifest drives downstream indexing and replay. (docs.langchain.com)

Implementation notes:

• Use schema validators at every handoff. Prefer machine-checkable contracts (OpenAPI, JSON Schema, zod). (openai.github.io)

• Version prompts and model selection. Record which prompt template and model version handled each step so you can reproduce runs. (docs.langchain.com)

• Design for replayability by storing raw model inputs and outputs; that allows offline replay with patched prompts or different models for debugging. (docs.langchain.com)

Quality control and failure modes

Agentic systems fail in characteristic ways. Anticipating these failure modes and instrumenting checks is essential to maintaining production safety and reliability. Below are common failure modes and concrete QA steps to catch or mitigate each. (arxiv.org)

1. Hallucination / content fabrication: the agent returns plausible-sounding but incorrect facts. QA steps: require source spans/provenance for every asserted fact; run automated cross-checks against trusted knowledge bases; require the model to set a confidence flag and force human review when below threshold. Maintain a denial-of-service safe fallback (e.g., return “uncertain” rather than fabricate). (arxiv.org)

2. Format drift: the LLM returns valid content that violates the downstream schema. QA steps: validate outputs with strict JSON schema validators and reject or re-run extraction when validation fails; include a grammar or canonicalizer step that normalizes values before commit. Log schema violations and block commits until fixed. (openai.github.io)

3. Tool misuse or side effects: the agent calls tools incorrectly or triggers unintended side effects (e.g., multiple writes). QA steps: implement idempotency keys, require a pre-flight dry-run for write operations, and keep tool interfaces minimal and well-typed. Use a sandboxed environment for testing. (openai.github.io)

4. Error propagation and cascading failures: an upstream mistake (bad extraction) leads to downstream incorrect actions. QA steps: implement independent verification agents that re-check critical facts, put gating checks between stages, and maintain short re-check loops instead of propagating unverified data. (docs.langchain.com)

5. Non-deterministic outputs causing flakiness: differing results across runs reduce reliability. QA steps: capture random seeds or model parameters, fix sampling parameters for critical steps (use temperature=0 or deterministic modes), or use a voting/ensemble across models to stabilize outputs. Record all model hyperparameters in the audit trail. (docs.langchain.com)

Operational QA checklist (short):

• Schema validators on every handoff and tool interface. (openai.github.io)

• Automated contradiction checks and provenance requirements for assertions. (arxiv.org)

• Replayable traces: store prompts, responses, tool calls, and system logs. (docs.langchain.com)

• Human-in-the-loop gates for low-confidence or high-risk decisions. (docs.langchain.com)

• Automated test suites that exercise edge cases and adversarial inputs. (docs.langchain.com)

FAQ

What are agentic workflows and when should I use them?

Agentic workflows are multi-step systems where LLM-powered agents interact with tools and deterministic logic to solve tasks. Use them when tasks require sequential decision making, tool calls, or when splitting responsibilities into focused nodes reduces complexity or risk (for example, document processing, autonomous task execution, or complex customer workflows). They are particularly useful when observability and replayability are required. (docs.langchain.com)

How do I choose between a single agent and a multi-agent (graph) design?

Choose multi-agent (graph) designs when you need specialization, conditional routing, parallel work, or per-node observability. Single-agent flows can be simpler for small tasks but become brittle as responsibilities grow. If you foresee needing separate tool sets, different verification strategies, or independent deployment/versioning for parts of the pipeline, adopt a graph-based workflow. (blog.langchain.com)

How should I validate model outputs automatically?

Use strict schema validators, independent re-check agents, and cross-references to curated knowledge sources. For actions with side effects, require a deterministic pre-flight check or dry-run and idempotency keys. Store diagnostics like confidence indicators, evidence spans, and tool outputs to support automated gating. (openai.github.io)

What observability features are most important for agentic workflows?

At minimum: structured traces of each LLM call (prompts and raw outputs), tool call logs (inputs, outputs, latency), model and prompt versions, and schema-validation results. These data enable offline evaluations, continuous testing, and safe rollbacks. Modern agent engineering platforms include built-in observability to capture these artifacts. (docs.langchain.com)

How do I reduce hallucinations in multi-step agents?

Require provenance for claims, use independent verification steps, limit open-ended generation where possible, and prefer deterministic sampling settings for critical stages. When uncertainty is unavoidable, surface confidence flags and route to human review. Design prompts that ask the model to cite source spans and run automatic checks against those spans. (arxiv.org)

Evelyn Carter

I write about turning AI from a fragile experiment into something teams can rely on every day. My focus is on prompt engineering, agentic workflows, and production systems—showing how to design, test, version, and scale AI work so it stays consistent, repeatable, and useful in real businesses.