Turn AI coding agents from a side experiment into a governed, measurable part of how your engineering org ships.
Agentic Engineering Consulting
Embed agentic AI into your engineering org. We help teams adopt AI coding agents, build evals, design agentic workflows, and ship autonomous systems built on top of our products.
The challenge
Most engineering teams have tried AI coding agents, but adoption stalls in a messy middle: a few enthusiasts get value, everyone else hits inconsistent results, and leadership can't tell whether the spend is paying off. Worse, agents get wired into real codebases and production systems with no evals, no guardrails, and no observability — so nobody trusts them with anything that matters. The result is either shadow usage that creates risk, or cautious paralysis that leaves the productivity gains on the table.
How we help
We treat agentic engineering as an org change, not a tool install — we assess where agents actually fit in your SDLC, then roll them out with the evals, guardrails, and observability needed to trust them on real work. Because we build our own agent stack, we bring battle-tested patterns rather than slideware: custom agents and tool integrations on BotManor (multi-LLM orchestration, RAG, MCP), agentic workflow orchestration on FluidGrids, and a developer control plane for sessions, environments, and agent runs via VibeControls — with BigConsole consoles that make agent activity, cost, and quality visible to engineering leaders. You keep your existing IDEs and CI; we make the agents reliable, observable, and safe inside them.
New: AI Engineering, explained in 2 min 17 sec
Make your AI a helper you can trust with real work.
AI engineering is the work that turns a clever AI demo into a helper you can trust with real work: hand it your own documents, test it, give it rules and keep checking it.
What you get
- An agentic-readiness assessment mapping high-value use cases across your SDLC and the guardrails each one requires
- A rollout and enablement plan for AI coding agents — tooling choices, access model, prompting/workflow standards, and team onboarding
- Custom agents and MCP tool integrations wired to your repos, services, and internal systems (built on BotManor)
- An evaluation harness with task suites, scoring, and regression checks so agent quality is measured, not assumed
- Guardrails and policy controls — scoped permissions, human-in-the-loop gates, and review/approval checkpoints
- Agentic workflow orchestration for multi-step engineering tasks, implemented in FluidGrids
- Observability and leadership dashboards for agent runs, cost, and outcomes (VibeControls control plane + BigConsole consoles)
- Runbooks, internal docs, and a trained internal champion so your team can operate and extend the system after handoff
How we engage
Discovery & readiness assessment
We audit your SDLC, codebases, security posture, and current AI usage to identify where agents create real leverage and where they introduce risk. The output is a prioritized roadmap with clear use cases, guardrail requirements, and success criteria.
Pilot on a high-value workflow
We pick one or two concrete workflows — test generation, refactors, code-review assist, or a custom internal agent — and ship them end to end with evals and guardrails from day one, so the pilot proves trust as well as productivity.
Build evals, guardrails & observability
We stand up the evaluation harness, scoped permissions, human-in-the-loop gates, and dashboards that make agent behavior measurable and safe — the foundation that lets you scale beyond a pilot with confidence.
Roll out & enable teams
We expand to more teams and use cases, set prompting and workflow standards, integrate agents into CI and developer tooling, and run hands-on enablement so adoption sticks instead of fading after the demo.
Handoff & continuous improvement
We document runbooks, train internal champions, and hand over ownership, with optional ongoing support to add use cases, tune evals, and keep pace as models and your needs evolve.
Outcomes you can expect
- Engineering teams adopt AI coding agents in a way that scales beyond early enthusiasts, with consistent practices across the org
- Leaders gain visibility into agent quality, cost, and impact instead of relying on anecdotes
- Repetitive engineering work — tests, boilerplate, migrations, reviews — is offloaded to agents so developers focus on higher-value problems
- Agents operate inside guardrails and evals, reducing the risk of low-quality or unsafe changes reaching production
- A reusable agentic foundation (agents, workflows, evals, observability) your team owns and can extend to new use cases
- Faster, more confident path from AI experimentation to durable, governed production use
Powered by our products
We deliver this faster and more reliably with the Algoshred product suite.
Frequently asked questions
No. We meet you where you are and integrate agents into your existing IDEs, repos, and CI/CD. Our products (BotManor, FluidGrids, VibeControls, BigConsole) add the orchestration, evals, guardrails, and observability layer — they don't force you to rip out your toolchain.
Engagements usually start with a short discovery and readiness assessment, followed by a focused pilot on one or two workflows, then a broader rollout. We scope each phase around your goals rather than a fixed package, so timelines depend on the number of teams and use cases — discovery and a meaningful pilot are typically the fastest way to prove value before committing to wider rollout.
You own the agents, prompts, workflows, evals, and any custom code we build for you, along with the runbooks and documentation. Where the solution runs on our products, you get the integrated system plus the knowledge and artifacts to operate and extend it; we make handoff and internal enablement an explicit part of the engagement.
Safety is built in from the pilot, not bolted on later. We use scoped permissions, human-in-the-loop gates, and review checkpoints so agents can only do what you've approved, and we back that with an evaluation harness and observability so you can see exactly what agents did and whether quality is holding up over time.
They're the foundation we build on, not a mandatory purchase for every engagement. BotManor powers custom agents and tool integrations, FluidGrids orchestrates multi-step agentic workflows, VibeControls is the developer control plane for sessions and agent runs, and BigConsole surfaces agent activity and cost to leadership. We'll recommend which pieces genuinely fit your needs, and consulting-only or hybrid arrangements are possible.
That's the point of the pilot phase. We define success criteria up front, instrument the pilot with evals and dashboards, and measure real outcomes on a high-value workflow. You make the scale-up decision based on evidence from your own environment rather than vendor promises.
Ready to get started?
Let's discuss how Agentic Engineering Consulting can help solve your problem and move your business forward.
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