AI Agent Development
AI Engineering

AI Agent Development

Agents that use tools, hold context, and actually ship.

Agents are easy to demo and hard to ship. The failure modes — hallucinated tool calls, context overflows, infinite loops, silent errors — are invisible until production. We've built production agents for healthcare, fintech, and enterprise ops. We've seen the failure modes so you don't have to.

35+
Agents in production
99.1%
Tool-call success rate
4–12 wk
Typical timeline
<0.3%
Hallucinated tool calls
Client outcome
99.1% tool-call accuracy across 35+ production agents.

Measured across similar ai engineering engagements we've shipped.

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What we build

01
Tool-using agents

Agents that call APIs, read databases, write files, and execute code — with idempotency, retry logic, and rollback built in at every step.

02
Multi-agent orchestration

LangGraph-based orchestrators that route tasks across specialized sub-agents with shared memory, typed handoffs, and structured checkpoints.

03
Memory & retrieval

Short-term conversation buffer, long-term vector + graph memory, and episodic recall — so your agent learns from past interactions without bloating the context window.

04
Eval & replay harnesses

Golden traces, adversarial probes, and load tests that stress the agent before production traffic arrives. You know the blast radius before you flip the switch.

05
Human-in-the-loop design

Approval queues, interrupt points, and full audit trails — so humans stay in control of high-stakes decisions without being in the critical path of routine ones.

06
Observability & cost controls

Per-agent token budgets, cost alerts, trace logging, and quality dashboards that surface failures before they escalate to your users.

How we Deliver

Week 1
Scope & failure mode mapping
We map every tool call, state transition, and failure mode before writing code. Agents without a failure mode map don't ship reliably.
Week 2–3
Tool skeleton & eval harness
Tool stubs, mock integrations, and a golden trace eval set built in parallel. We test before we build.
Week 3–8
Agent loop & integration
The full agent loop — reasoning, tool selection, result parsing — wired to real production APIs with real auth and real error handling.
Week 8+
Harden & monitor
Load tests, adversarial inputs, cost caps, and monitoring. No agent goes live without a known blast radius and a runbook.
Evolve Edge team

From Evolve Edge

“We don't ship AI without an eval harness. Not because clients ask — because it's the only way to know the system is actually working in production.”

FAQ

LangGraph or custom orchestration?
LangGraph for most cases — the graph model maps cleanly to agent workflows and the tooling is mature. We go custom when state complexity or performance requirements exceed what LangGraph handles cleanly.
How do you handle hallucinated tool calls?
Structured output parsing with Pydantic, retry with exponential backoff, and a maximum-attempt circuit breaker. Every failure is logged with full trace context so you can inspect what happened.
What's the difference between an agent and a chatbot?
A chatbot responds. An agent acts. Agents plan across multiple steps, call real tools, manage state, and recover from failures — without a human in the loop for every decision.
Can agents access our internal systems?
Yes. We've connected agents to ERPs, EHRs, CRMs, and custom databases. Every integration gets idempotency, scoped auth, and immutable audit logging.

Related services

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A senior engineer will review your project and reply within one business day with a clear next step.