Managed AI Agents for Professional Services Firms

Business AI use is moving into recurring workflows

Individual AI use still matters. Staff learn where a model helps, how to provide context and how to check the response. Firms can now apply those lessons to work that repeats across projects, clients and teams.

An agent can take responsibility for part of that recurring work. It might review new tender documents, assemble the relevant project experience, prepare a bid summary and send the pursue decision to a practice leader. Another might watch project actions and prepare a weekly exception report for the project manager.

The job remains bounded. The agent works within an agreed process and brings decisions to the right person. This makes the work easier to inspect, improve and measure.

What is a managed AI agent?

An AI agent has a defined job, access to approved information and tools, instructions for completing the work and rules for when a person becomes involved. It can gather information, reason across it, prepare an output and, where authorised, take an action in another system.

A managed AI agent is maintained over time. Someone reviews how it is performing, corrects failures, updates its knowledge and changes its permissions or instructions when the business changes. The agent has an operating owner in the same way that an important reporting process or business application has an owner.

Consider a commercial agent working across project notes, correspondence, budgets and scope. It sees evidence that a client request may sit outside the agreed scope, assembles the supporting records, estimates the possible effect and asks the project manager whether a variation should be drafted. The project manager retains the commercial decision and approves any client communication.

A useful operating sequence is: observe, prepare, recommend, human approves, act. Some low-risk work may need fewer approval points. A formal commitment, technical judgement or external communication usually needs more.

Why professional services firms suit this model

Engineering, property, consulting, accounting, financial services, legal, architecture and advisory firms produce document-heavy work. They hold project records, proposals, correspondence, meeting notes, research, templates, standards and commercial information across several systems.

Much of the work follows a recognisable pattern while still requiring professional judgement. People retrieve information, compare it with requirements, prepare a first pass and ask an experienced colleague to decide or approve. Agents can support the retrieval, comparison and preparation steps.

Senior staff often carry context between systems and people. They remember which project is relevant, where the final document sits and why an earlier decision was made. A well-designed agent can assemble that context with source references, giving the professional more time to interpret it and decide what happens next.

The quality of the source material sets a practical limit. Missing project records, inconsistent naming and outdated templates will weaken the output. Early agent work often reveals information and process problems that the firm needs to fix.

Examples of AI agents in professional services

An opportunity agent can review an incoming tender, summarise the requirements, find relevant experience and staff, flag missing information and prepare a pursue recommendation. Leadership still decides whether the opportunity fits the firm’s strategy and capacity.

A proposal agent can use approved past proposals, project examples, staff CVs and company material to prepare a first draft. It can track response requirements and identify claims that need evidence or technical review before issue.

A project knowledge agent can work across project files, correspondence, meeting notes and decisions. Team members can ask when a decision was made, which document supports it and which actions remain open, with links back to the source records.

A project management agent can track commitments, milestones, risks and overdue work from meetings and project systems. It can prepare updates, route actions to owners and bring exceptions to the project manager.

A QA and compliance agent can compare a document with the firm’s templates, client requirements and approved criteria. It can flag missing sections, conflicting figures or unsupported statements for a qualified reviewer.

A commercial agent can monitor budgets, work in progress, scope changes and project activity. It can prepare evidence of a possible variation or margin issue and place it with the accountable project leader.

A client intelligence agent can assemble account history, meetings, correspondence and previous work before a client conversation. It can also identify agreed follow-ups that have not been completed.

A research agent can run recurring technical, industry, regulatory or market research using approved sources. It can record what changed, cite the evidence and route material changes to the relevant specialist.

Design agents around the work

Department labels are a poor map for many business agents. Winning a new project can involve business development, technical specialists, finance, marketing and leadership. A useful proposal agent may need approved inputs and review from all five.

Start with a named workflow and output. Define the trigger, source information, steps, judgement points, systems, handovers and accountable owner. The agent’s job can then cover a coherent part of the work, even when that work crosses team boundaries.

This approach also makes ownership clearer. A practice leader may own the pursue decision, a bid manager may own the proposal process and a technical director may approve the final claims. The agent supports each person at the agreed point.

A firm may eventually run 3, 5 or 10 agents across recurring work. Each addition should earn its place through a defined result, usable information and an owner who will manage it.

Keep human judgement at the decision points

Professional services firms carry commercial, technical, contractual and reputational responsibility. Agent design needs to name the person who holds that responsibility and show exactly when the work reaches them.

Common decision points include pursuing an opportunity, approving a proposal, issuing a variation, making a technical judgement, sending an important client message, approving expenditure and making an employment decision. The person needs enough evidence to review the recommendation. A simple approve button provides too little context.

Approval also needs capacity. Routing every minor action to one senior person can create a new bottleneck and encourage quick, low-quality review. The workflow should separate routine, reversible activity from decisions that require authority or professional skill.

Good records make oversight workable. Keep the sources used, the agent’s proposed action, the reviewer’s decision and any action taken. This helps the firm investigate errors, improve the instructions and show how an important outcome was reached.

Why AI agents need ongoing management

An agent can pass its launch test and still become less useful. Source documents change, staff roles move, systems are updated, client requirements differ and the underlying AI models improve. New edge cases appear once real people use it on real work.

Managed operation covers the practical work after deployment: improving instructions, maintaining knowledge sources, reviewing permissions, monitoring failures, checking outputs, maintaining system connections and managing usage and cost.

The owner can add approval points after a failure, remove access that the agent no longer needs and test a newer model against the current one. They can also identify an adjacent task the agent could take on once the original job is working reliably.

Review the business result as well as the technical result. Useful measures may include hours saved, proposal turnaround time, project administration reduced, access to project knowledge, rework, variations identified, project margin, client response time and tender capacity. Measure the few that connect directly to the agent’s job.

This creates a managed agent service: continuing responsibility for the agent as a business system, including its performance, controls, cost and improvement plan.

A practical way to start

Begin with an agent opportunity assessment across the firm’s major workflows. A longlist of 10 to 20 possible agents is usually enough to see the pattern. Assess each opportunity against frequency, time spent, business effect, data availability, implementation effort, risk and the need for human judgement.

Choose a small number with clear outputs and committed owners. Define each agent’s job, approved information, system access, permitted actions, human approval points, quality test and exception route.

Build and test the first agents with real users and real work. Keep permissions narrow during the trial and compare the result with the current method. Record failures, rework, reviewer effort, elapsed time and any change in the finished output.

Move successful agents into managed operation with an owner, usage and quality measures, a review schedule and a process for changes. Stop the agents that create more work than they remove.

Considering where managed AI agents could fit across your business? Addaptive works with Australian organisations to identify agent opportunities, redesign the workflow, build the agent and manage it after launch.

• Assess recurring workflows and create an opportunity longlist.
• Rank the opportunities by business effect, feasibility and risk.
• Define jobs, information, access and human decisions.
• Test a small portfolio on real work.
• Measure finished work, quality, review effort and cost.
• Manage and improve the agents after launch.

Frequently asked questions