Before you build
You don’t know where to start. The workflow exists, the agent question is open.
Agent Opportunity Review
Outcome: a clear next step. What to build, what to skip, what shape it takes.
Review the workflowThe engineering behind AI agents that actually ship.
From “should we build this” to “why is it failing in production”. The full agent stack: strategy, architecture, evals, implementation, ops.
Share your workflow, current stage, and the failure that worries you most.
The four decisions behind every useful agent
A useful agent is a workflow,
not just an LLM.
Fit
Should this be an agent, a copilot, an automation, or a human workflow?
Boundary
What can it read, remember, write, or execute?
Proof
What evals, tests, traces, and reviews show that it works?
Launch
What must be true before this touches production?
Where are you now?
Where you are now.
Prototype exists
It works in demos. Reliability, tool use, memory, or retrieval behavior is unclear.
Agent Readiness Sprint
Outcome: architecture, tool boundaries, eval plan, and a roadmap.
Assess the prototypeBefore launch
The agent is about to touch real users, customer data, or internal tools.
Production Readiness Audit
Outcome: launch blockers, eval gaps, observability gaps. Plus a go/no-go call.
Find launch blockersKnown gaps
You know you need evals, traces, regression tests, guardrails, or operational checks.
Hardening Sprint
Outcome: eval harnesses, regression tests, traces, and guardrails. In your codebase.
Harden the systemWhat the work looks like
Across the whole stack. Strategy to launch ops, every layer between.
- Strategy
- The agent-or-automation call, scope, and boundaries. With reasoning, not slides.
- Architecture
- Runtime, memory, retrieval, and tool interfaces. Written down for your team to own.
- Evals
- Ground-truth sets, regression runners, drift checks. Committed to your codebase.
- Implementation
- The harnesses, tracing, and guardrails built into the codebase you ship.
- Red teaming
- Prompt injection, jailbreaks, tool misuse. Adversarial scenarios run and documented.
- Stress testing
- Load and concurrency against the runtime. Race conditions, retry storms, scale failure.
- Audits
- Structured review of an existing agent. Gaps, blockers, risk, with a go/no-go.
- Launch ops
- Observability, rollback paths, runbooks. The work that decides whether launch survives.
Common problems we catch
These are the patterns we look for first.
Workflow, not agent
The system should be a workflow, not an agent.
Tool over-permission
Tool permissions are broader than the agent’s reliability.
Demo retrieval
Retrieval works in demos but fails on edge cases.
Memory misuse
Memory stores the wrong information or persists too much.
Prompt-only tests
The team tests prompts, not full agent trajectories.
Late-stage approval
Human approval happens after the dangerous step.
Start here
Book a 30-minute call.
No deck. No NDA. No paperwork.
Bring the workflow, a prototype, the failure that worries you most, or just the question. Walk away with a yes-or-no on the agent. A yes-or-no on us.