Building Agentic Products: A 2026 Guide
Choose orchestration patterns, connect tools, and evaluate agentic products with step-level checks and product metrics.
Prerequisites
- • ai-literacy-for-pms
What an agentic product needs
An agentic product needs a way to organize work, call tools, and check results. A chat interface around a single model call does not provide those controls by itself. The SDKs below supply different parts of that infrastructure; the product team still needs to define success and test the workflow.
The Three Patterns
These three patterns offer starting points for organizing agent work.
Pattern 1: Sequential (Relay Race)
One agent does its job, hands off to the next. Each step depends on the last.
When to use: linear workflows with clear stages. Research, then analysis, then writing. Intake, then triage, then resolution.
Example workflow: A planning agent designs a change, an implementation agent writes it, and a review agent checks the diff. Each stage may need several model calls and tool actions.
Watch for: errors carrying into later steps. If four steps each succeed 90% of the time independently and all must succeed, the combined rate is 0.9⁴, or about 66%. Real errors may be related, so measure the full workflow as well as each step.
Pattern 2: Parallel (Specialist Team)
Multiple agents work on independent pieces at the same time. Results merge at the end.
When to use: independent subtasks. Research five competitors. Analyze five datasets. Generate five variants.
Example workflow: Agents research separate topics, then their results are combined into one report. Check for dependencies before starting tasks in parallel.
Watch for: duplicated work and context. Parallel tasks may repeat research or process the same material. Measure total cost and set a budget for the workflow.
Pattern 3: Hierarchical (Orchestrator and Workers)
A planner agent decides what to do and delegates to specialist agents. The planner reads results, replans if needed, returns the final output.
When to use: complex tasks with unknown shape. The plan emerges from the work.
Example workflow: A planner assigns file analysis, implementation, and review to specialist agents, then decides whether more work is needed.
Watch for: runaway loops. Hierarchical agents can loop on themselves for hours if you don't cap iterations. Set a max-step limit and a kill switch.
Picking a Pattern
A simple decision tree:
| Workflow shape | Pattern |
|---|---|
| Linear, predictable | Sequential |
| Independent subtasks | Parallel |
| Adaptive, exploratory | Hierarchical |
Patterns can be combined. Start with the fewest stages and agents that meet the task requirements, then add complexity when testing shows a need.
MCP: connecting tools and data
MCP provides a shared protocol for connecting agents to tools and data. Check whether your chosen clients support the server capabilities, transport, and authentication your product needs. A compatible tool description does not by itself grant permission to use an API.
The protocol's March 2026 roadmap identifies remote-service scaling, agent communication, governance, and enterprise needs as priorities.1 It describes work on Streamable HTTP and session management; roadmap proposals should not be treated as shipped features.
PM Toolkit exposes calculator tools through MCP. For your own product, compare an MCP integration with direct API access based on the clients and workflows you need to support.
Five agent frameworks to compare
| Framework | What it provides | Consider it when |
|---|---|---|
| Claude Agent SDK2 | Claude Code's agent loop, tools, and context management | You want to build around Claude Code capabilities |
| OpenAI Agents SDK3 | Agents, tools, handoffs, guardrails, and tracing | You want these pieces in a lightweight agent framework |
| Google ADK4 | Agent and workflow development tools | Its workflow and deployment options fit your stack |
| LangGraph5 | Stateful workflows, durable execution, and human review | You need explicit control over a long-running workflow |
| CrewAI6 | Agent crews and event-driven flows | Its role and workflow structure fits the task |
Compare tool support, state handling, deployment, debugging, and costs with a small version of your workflow. Model-provider fit is one factor; it does not establish which framework will require the least work.
Build vs Buy
Three implementation options in 2026:
Buy a vertical agent. Cursor for code, Claude Code for terminal-native dev, Harvey for legal, Devin for autonomous engineering tasks. If your team needs the workflow and someone shipped it, use theirs.
Build with an SDK. You own the prompts, the evals, the user experience. The vendor owns the model and the agent runtime. Best path for differentiated products.
Build from scratch. You'll need to write the orchestration loop, retry logic, eval harness, and cost controls yourself. Take this route only if your workflow is new enough that existing frameworks prevent you from building it.
Prefer an existing product or SDK when it fits the workflow. Building orchestration from scratch adds maintenance work that needs a clear justification.
Eval Design for Multi-Agent
A multi-agent workflow can fail within a step or when passing work between steps.
Three evals you need.
1. Step-Level Evals
Each agent in your pipeline gets its own golden set. If the research agent's job is to summarize a doc, you have 50 hand-labeled summaries. If the planner's job is to break a task into steps, you have 50 task-to-steps examples.
These examples help locate regressions when the workflow changes.
2. Handoff Evals
When agent A passes to agent B, what context does B need? What context does B mishandle? Test the seams, not just the parts.
A common failure: agent A's output is verbose, agent B's context window fills with low-signal text, agent B misses the key information. Step-level evals would not catch this. Handoff evals do.
3. End-to-End Evals
The full pipeline against a real user task. This is your task completion rate metric.
End-to-end evals are expensive to build and slow to run. Choose a schedule that fits their cost and the risk of the change. Include them before releases.
The Four Metrics That Matter
Start with these four and set targets for the task and customer expectations:
| Metric | Definition | What to check |
|---|---|---|
| Task completion rate | Completed tasks / started tasks | Define completion and inspect failures |
| Time to first useful output | Time from request to an actionable response | Compare with the user's needs, including slower cases |
| Retry rate | Share of tasks requiring a repeated request | Distinguish retries from new work and choose a suitable time window |
| Cost per completed task | Total workflow cost / completed tasks | Include failed attempts, model calls, tools, and infrastructure |
Use per-step accuracy and other engineering measures to investigate these outcomes. They help explain failures but do not replace checking whether the task was completed.
Common pitfalls in agentic products
Check for these problems during testing:
1. Context obesity. Each agent passes everything it has to the next. Context windows fill, signal drops, accuracy crashes. Fix: pass only the keys the next agent needs.
2. No kill switch. Hierarchical agents loop. The user watches their token bill climb. Fix: hard cap on iterations and tokens per session. Show the user the meter.
3. Unsupported factual claims. An agent can act on a confident but wrong answer. Give it access to relevant sources and test whether its claims and actions are supported. Citations alone do not establish accuracy.
4. Hand-offs without contracts. Agent A produces free text, agent B parses it heuristically, parsing breaks silently. Fix: use structured outputs (JSON schema) at every handoff. Check your SDK's structured-output support and validate outputs before another step uses them.
5. No observability. When the agent fails, no one knows where. Fix: log every step with input, output, latency, cost. Choose logging that helps trace failures while protecting sensitive inputs and outputs.
A 30-Day Roadmap
If you're starting from scratch.
Week 1: Prototype. Pick one workflow. Pick a pattern (default to Sequential). Wire two agents together with the Anthropic or OpenAI SDK. Use MCP for tools. Ship to internal users.
Week 2: Safety. Add a kill switch, iteration cap, token budget. Add structured-output contracts at every handoff. Add basic logging.
Week 3: Evals. Build a step-level golden set for each agent. Build one end-to-end eval. Run them on every change.
Week 4: Metrics. Wire up the four metrics. Set thresholds. Add a feature flag.
Then ship to a small percentage of real users behind the flag and watch the metrics.
What I would do differently in 2026
If I started a new agentic product today:
- Compare models on representative tasks, cost, and response time before choosing a default.
- Use MCP where it fits the tool integration and client requirements.
- Structured outputs everywhere.
- Observability before features.
- Test individual steps and the complete workflow.
- Buy the vertical agent if one exists. Compete on workflow, not on model.