AI agents,
engineered as software.
Reasoning, tool use, memory, orchestration, evaluation and human-in-the-loop controls, designed against the risk of the workflow, and shipped with the observability any production system deserves.
A complete agent practice, engineered end to end.
From copilots and workflow agents through multi-agent orchestration, tool use, memory, evaluation and guardrails, our practice covers the full delivery surface of production AI agents.
Agent strategy & opportunity mapping
Which workflows genuinely warrant an agent, which are better served by a script or a form, and which shouldn't be automated at all. We frame the use case before we build one.
Domain assistants & copilots
In-product and internal copilots, grounded in your systems, scoped to real user jobs, that draft, summarise and act, with citation, provenance and a clear undo path.
Workflow & task agents
Agents that pick up tickets, triage queues, complete multi-step operational tasks and hand back to humans, engineered with deterministic checkpoints, not built on a single overconfident prompt.
Multi-agent orchestration
Planner/executor and specialist-team topologies where they earn their keep, with typed hand-offs, budgets and stop conditions, not built as a swarm of prompts that talk to themselves.
Tool use & system integration
Typed tools, function calling, MCP servers, browser and desktop control, with schemas, scopes and audit trails, so agents work through the same interfaces your engineers would.
Memory, state & context
Short-term working memory, long-term knowledge, session state and structured recall, designed against privacy, tenancy and cost boundaries so the agent's memory is an asset, not a liability.
Voice & multimodal agents
Voice, telephony and multimodal agents on real-time speech and vision stacks, with turn-taking, barge-in, latency budgets and the guardrails a customer-facing channel actually requires.
Agentic automation & RPA hybrids
Blends of deterministic RPA and agentic reasoning, so the boring 80% stays boring and reliable, and the reasoning is applied to the 20% that actually needs it.
Human-in-the-loop & handover
Escalation, approval and review flows calibrated to risk, with clear queues, SLAs and disagreement paths, so humans stay in the loop where it matters and out of it where it doesn't.
Evaluation & red-teaming
Task-level evals, trajectory evaluation, adversarial red-team sets and regression suites, treated as first-class engineering artefacts, gating deploys the same way tests gate code.
Guardrails, safety & tool scoping
Prompt-injection defence, output policy, tool scoping and rate limits, engineered proportional to blast radius, so an agent given access to a system can't quietly do more than we intended.
Observability & agent operations
Trace-level observability of every step, tool call, cost and outcome, so agents in production are debuggable, tunable and improvable in the same way any distributed system is.
Frame the workflow. Ship what survives an eval.
A delivery model built for the shape of agent work, honest framing of what warrants an agent, fast prototyping against real tasks, and evaluation as the gate to production.
Frame
A working session to sharpen the target workflow, define what a good outcome looks like, audit the systems the agent has to talk to, and agree the risk boundary. The output is a phased plan with an eval already sketched.
Prototype
A rigorous prototype against real tasks and a real eval, not a demo. We size the model, the topology (single agent, planner-executor, multi-agent) and the tool surface before anything is built to last.
Productionise
Iterative delivery of the full stack, orchestration, tools, memory, guardrails, observability and human-in-the-loop, integrated with your systems. Deploys are gated by evals, not by opinions.
Operate
Trajectories, cost, quality and drift monitored as ongoing engineering disciplines. Prompts, tools and policies are versioned; regressions are caught in CI. Knowledge transfer is designed in from day one.
A pragmatic agent-engineering toolchain.
Model, framework and orchestration selection is a working decision, driven by the workflow, your existing systems and long-run economics, not by whichever framework was posted this week.
Python
TypeScriptAAnthropicOOpenAI
LangChainLLangGraphLLlamaIndexCCrewAIAAutoGenMMCP
Hugging FacePPineconeWWeaviatePpgvector
Redis
KafkaTTemporalLLangSmithLLangfuse
MLflow
Kubernetes
AWS Bedrock
Azure OpenAI
Vertex AITTwilio
PlaywrightProgrammes delivered on this practice.
What prospective clients ask us first.
If your question isn't addressed here, please reach us at hello@tekansa.com.
01When is an agent the right answer, and when is it not?
An agent earns its place when the workflow needs planning, tool use and adaptation to a variable context, a triage queue with heterogenous cases, a research task, a customer conversation that branches. When the workflow is a fixed sequence of steps with a stable schema, a script, a rule engine or a plain RPA bot will be cheaper, faster and more reliable. Our first job on any engagement is to be honest about which shape the work is, and to build the simplest thing that meets the outcome.
02Single agent, planner-executor or multi-agent?
A working decision, not a preference. Most useful production agents are single-agent systems with a well-designed tool surface and a clear scope, that's the shape we default to. Planner-executor splits help when the reasoning and the action steps have very different latency, cost or trust profiles. Multi-agent topologies pay off when the domain genuinely has specialist roles with typed hand-offs, and cost money everywhere else. We pick per use case, based on the task, the risk envelope and the honest evidence from the prototype.
03How do you handle safety, prompt injection and tool-use risk?
Layered defence, proportional to blast radius. Tools are scoped and typed; destructive actions require confirmation or human approval; inputs pass through prompt-injection and out-of-scope filters; outputs run against policy and factuality checks. Every trajectory is logged with the inputs, tool calls, outputs and cost, so incidents can be reproduced and post-mortemed. Nothing that touches sensitive systems ships without a red-team pass, and the guardrail stack itself is versioned and evaluated like any other engineering artefact.
04How do you evaluate agent quality when the trajectories vary?
By separating task-level outcomes from trajectory-level behaviour. Task-level evals grade the final result against a rubric, did the ticket get resolved, did the summary match ground truth, did the booking complete. Trajectory-level evals grade the process, tool calls, budget, safety, disagreements, using a mix of programmatic checks and LLM-as-judge calibrated against human graders. Both sets run in CI, gate deploys and are extended over time with the interesting cases production surfaces.
05How do you handle human-in-the-loop and escalation?
Human oversight is designed for the specific risk of each step, not applied as a blanket. Low-stakes actions execute autonomously with post-hoc review; higher-stakes actions require confirmation; edge cases and disagreements route to a human queue with the trajectory attached, so the reviewer sees why the agent hesitated. Handover is a first-class engineering surface, not a modal, and the feedback from human reviews is captured back into evals and prompts so the system actually gets better over time.
06How do you manage cost and latency in production agent systems?
As real engineering budgets. Each surface gets a cost-per-task and p95 latency envelope, and the stack is chosen against those numbers, routing between models, caching, streaming, smaller distilled models where they hit quality, self-hosted inference where volume earns it. Trajectories are instrumented at the step level, so cost and latency regressions are visible per tool, per model and per version, trade-offs stay negotiable rather than surprising at the invoice.
Let's discuss your agent programme.
Share a brief overview of the workflow you're trying to automate and we'll arrange an introductory conversation with the relevant practice leadership. Every enquiry receives a direct response.