Accelerator

Agent Foundry — tool-using AI in 14 days

A production AI agent integrated into your stack — with evals, observability, and guardrails.

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Building an AI agent is easy. Building one that works reliably in production, recovers from failures, stays within cost budgets, and improves over time is a different problem entirely. Agent Foundry is our 14-day accelerator that delivers a scoped, instrumented, production-ready agent — not a demo notebook.

14
days to production
6
core deliverables
p95
latency SLO included
Client outcome
Median time from first token to production deployment: 12 days.

Measured across similar accelerator engagements we've shipped.

Get a proposal
StackLangGraphOpenAI / AnthropicTypeScriptPythonPostgresRedisOpenTelemetry

What we build

01
Agent design & tool definition

We scope the agent's objective, define the tool surface, and design the decision loop with failure modes mapped before a line is written.

02
Tool integration & retrieval

Web search, database queries, API calls, code execution — every tool is typed, tested, and sandboxed with retry and fallback logic.

03
Eval harness

A test suite that scores agent outputs against ground-truth examples. You own the evals — not just the model.

04
Observability & cost controls

Every LLM call traced with OpenTelemetry: token counts, latencies, tool call chains, and cost per run — all queryable.

05
Human-in-the-loop hooks

Approval gates, confidence thresholds, and Slack/email escalation paths for decisions that should stay with humans.

How we Deliver

Day 1–3
Agent scoping
Define the task, tool surface, data sources, and success criteria. Output: a written spec and acceptance tests.
Day 4–8
Core agent build
Tool implementations, decision loop, memory and retrieval integration. Agent runs end-to-end by day 7.
Day 9–12
Eval & hardening
Run evals against ground truth, fix failure modes, instrument observability, tune prompts for cost/quality.
Day 13–14
Deploy & handover
Production deployment, runbook, cost dashboard, and a full knowledge transfer to your engineering team.

Best practices for Agent Foundry

  • Define tool schemas before writing agent code

    Ambiguous tool boundaries are the most common source of production failures. A clear schema surfaces edge cases before any code is written.

  • Build your eval set before tuning prompts

    Without a baseline, you can't tell if a prompt change improved anything. Evals first, optimization second.

  • Log every LLM call with full input and output from day one

    Retroactively adding structured logging after a production incident is far more painful than building it in at the start.

  • Start with one tool, one task

    A single-tool loop that reliably passes evals is a stronger foundation than a five-tool system that passes sometimes.

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

Which LLM providers do you use?
We default to Anthropic Claude and OpenAI, with provider failover built in. We also support Gemini, Bedrock, and local vLLM deployments.
What if the agent needs to access our internal data?
We integrate with your existing data stores — Postgres, S3, internal APIs. All connections are read-only unless the spec requires writes, with full audit logging.
How do you handle prompt injection and adversarial inputs?
Input sanitization, output validation, sandboxed tool execution, and a guardrails layer are included by default.
Can you build multi-agent systems in 14 days?
For orchestrated multi-agent work, we scope carefully on day 1. Simple two-agent pipelines fit in 14 days; larger systems move to a 30-day engagement.

Have Questions? Let's Talk.

Free 30 minute call with a senior engineer, not a salesperson. We have got the answers to your questions.

Book strategy call
contact@evolveedge.co+1 (512) 678-3820