Agentic AI
AI systems that plan, use tools, and complete multi-step tasks on their own. The technology is advancing fast, but production deployment demands careful safety design.
ClearView
Software that reasons and acts
Traditional software follows predetermined paths. An agentic system chooses its path at runtime: it decides which tools to call, what information to gather, when to retry, and when to hand a decision back to a person. That shift moves the engineering effort from writing the steps to defining the boundaries the steps must stay inside.
The framework landscape is young and moving. LangGraph, CrewAI, Claude tool use and OpenAI function calling each carve up orchestration differently, and MCP is emerging as a standard way to hand agents their tools. Most of these interfaces will look different within two years, which argues for keeping the architecture independent of any one of them.
The hard problems sit above the frameworks. Testing a system that does not behave the same way twice, bounding what it may spend and touch, and producing an audit trail for every decision it takes: these determine whether an agent reaches production, and none of them is settled by picking a library.
An agent is not the default form for AI work either. A task a single model call can finish, classification, extraction, drafting, is served more simply by a direct call to Claude or a GPT model with structured output. And where a person must own the final decision anyway, an assistive pattern that prepares work for review is cheaper to build and easier to govern than an agent acting alone.
- Document Processing
- Code Review
- Workflow Automation
- Multi-Step Research
Our Agentic AI capabilities
Agent architecture design
Architecture is where an agent's risk is decided. The planning loop sets how the agent reasons between actions, the tool registry defines everything it can touch, the memory strategy determines what persists between runs, and the escalation policy names the moments a person takes over. We design those four against the consequence profile of the workflow before any framework is chosen, so the framework serves the design. The output is a written architecture that a client's engineers and reviewers can interrogate.
- ReAct pattern
- Tool registry
- Memory management
Tool integration and orchestration
A tool is an API with a stricter contract: typed inputs, validated outputs, and an explicit statement of what the agent is allowed to do with it. We build registries of such tools over client systems, databases, code interpreters and other agents, increasingly exposed through MCP so the same tool serves any agent that needs it. Orchestration then becomes an execution graph, built in LangGraph or with direct model APIs, with retries, fallbacks and timeouts handled in the graph rather than left to the model's judgement.
- LangGraph
- Function calling
- MCP
Safety, guardrails, and oversight
Guardrails are engineered in layers: input validation before the agent sees a request, output filtering before anything leaves, authority boundaries on every tool, cost ceilings per run, and circuit breakers that halt a loop that has stopped converging. Every decision the agent takes is logged with its context, so an auditor can replay why a given action happened. Human-in-the-loop checkpoints sit at the consequential steps.
- Audit logging
- Human-in-the-loop
- Cost controls
Why Adaca for Agentic AI?
Regulated-first design
Audit logging, authority boundaries, and human checkpoints built in before the first line of agent code.
Framework-agnostic approach
We evaluate LangGraph, CrewAI, and Claude tool use against your requirements. No vendor lock-in.
Production, not prototypes
Error recovery, cost controls, rate limiting, and observability. We move agents from notebooks to SLA-backed services.
Internal AI products
We build our own production AI tools including Lovelace, Primrose, and Lineer. Our advice comes from shipping, not reading.
End-to-end observability
Every agent run is traced: tokens consumed, tools called, decisions made, time elapsed. Cost ceilings prevent runaway spend.
Deterministic testing
Recorded tool responses, assertion-based evaluation, and adversarial input suites make non-deterministic systems auditable.
How We Staff It
Agent work is staffed in two layers. An architect defines the boundaries, the tool registry, the authority model and the escalation policy, while engineers build the tools and orchestration in Python or TypeScript, the languages the model SDKs and agent frameworks target. Both come from Adaca's existing engineering pool.
An engagement usually opens with agent discovery to establish which workflows reward an agent, and the build team then forms around what it finds. Engineers work to the customer's working day, inside your systems and change controls.
Common Questions
Which of our workflows are worth turning into agents?
The ones with volume, structure and a clear definition of done: work that recurs in a recognisable shape, produces artefacts that can be checked, and consumes enough time to repay the engineering. Agent discovery answers the question with evidence; it sits at the start of the Run stage of the AI Journey, Adaca's staged path from Crawl through Walk to Run, and it examines your real workflows rather than hypothetical ones.
A workflow can also fail the test. Where volume is low or each case is unlike the last, discovery says so, and the recommendation stops at a simpler form of automation.
How much authority should an agent be given?
As little as the workflow can stand at first, widened on evidence. Authority is expressed concretely: which tools the agent holds, what each tool may change, what it may spend per run, and which actions require a person's approval before they execute.
The boundaries are code, enforced by the application, so they hold regardless of what the model decides. Widening one is a reviewed change justified by the agent's measured record.
How do you test an agent before it goes live?
Against its own history. Adaca validates agents by running them against historical executions of the same or similar workflows, with requirements derived from those executions rather than written in the abstract. The agent's output is compared with what actually happened, and the gains are instrumented and measured before launch.
Nothing goes live without client sign-off on the measured results.
Which model should an agent run on?
Whichever passes the evaluation for each step, and it need never be only one. The model sits behind a seam in the orchestration layer, so a planning step, an extraction step and a drafting step can each run on the model that measures best for it, from Anthropic or OpenAI alike.
Prompts and evaluations are versioned per model, so re-pointing a step is a routing change followed by an evaluation run, and the agent's architecture outlives any one model generation.
What makes an agent system maintainable over time?
Keeping the non-deterministic surface small. In a well-built agent the model makes a narrow class of decisions, while tools, orchestration, guardrails and state are ordinary code in Python or TypeScript, testable in the ordinary way. Maintenance then looks familiar: typed tool contracts catch drift where the agent meets your systems, and traces show which decision changed when behaviour does.
Frameworks are held at arm's length for the same reason. The architecture, meaning the loop, registry, memory and escalation design, is the durable asset; the framework underneath it is replaceable.
Can Adaca supply agent engineers to a team we already run?
Yes. Agent engineers come from the same pool as Adaca's backend staffing, and a shortlist usually reaches you within 48 hours of a brief, drawing on people Adaca already employs or already knows.
Terms are three, six, nine or twelve months, chosen up front, and an engagement can start at one engineer.